{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 决策树DecisionTree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 sklearn中的决策树\n",
    "\n",
    "在$sklearn$中，决策树有两个不同的评估器：**DecisionTreeClassifier​**和**DecisionTreeRegressor**\n",
    "\n",
    "- $DecisionTreeClassifier$是分类树，解决的是分类问题\n",
    "- $DecisionTreeRegressor$是回归树，解决的是回归问题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这两个评估器，都在sklearn.tree模块内，大家可以看一下官网的详细介绍：https://scikit-learn.org/stable/modules/tree.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面我们主要讲解sklearn中的分类树。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 CART分类树的sklearn快速实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-29T07:27:25.852216Z",
     "start_time": "2024-02-29T07:27:25.844493Z"
    }
   },
   "outputs": [],
   "source": [
    "# 导入决策树分类器\n",
    "from sklearn.linear_model import LogisticRegression, LinearRegression\n",
    "from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-29T07:27:26.386172Z",
     "start_time": "2024-02-29T07:27:26.377932Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>income</th>\n",
       "      <th>credict_rating</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   income  credict_rating  class\n",
       "1       1               1      0\n",
       "2       2               2      0\n",
       "3       2               1      0\n",
       "4       1               2      1\n",
       "5       1               1      0\n",
       "6       1               2      1\n",
       "7       1               2      1\n",
       "8       2               1      0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 构建数据集A\n",
    "import pandas as pd\n",
    "\n",
    "df_A = pd.DataFrame({\"income\":[1,2,2,1,1,1,1,2]\n",
    "                     ,\"credict_rating\":[1,2,1,2,1,2,2,1]\n",
    "                     ,\"class\":[0,0,0,1,0,1,1,0]},index=[*range(1,9)])\n",
    "df_A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-29T07:27:26.801635Z",
     "start_time": "2024-02-29T07:27:26.785587Z"
    }
   },
   "outputs": [],
   "source": [
    "# 切分特征和标签\n",
    "X = df_A.iloc[:,:-1]\n",
    "y = df_A.iloc[:,-1]\n",
    "\n",
    "# df_A.iloc[行索引（位置）,列索引（位置）]\n",
    "\n",
    "# df_A.iloc[:,:-1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-29T07:27:27.159290Z",
     "start_time": "2024-02-29T07:27:27.141772Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>income</th>\n",
       "      <th>credict_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   income  credict_rating\n",
       "1       1               1\n",
       "2       2               2\n",
       "3       2               1\n",
       "4       1               2\n",
       "5       1               1\n",
       "6       1               2\n",
       "7       1               2\n",
       "8       2               1"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-29T07:27:27.496404Z",
     "start_time": "2024-02-29T07:27:27.488826Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    1\n",
       "5    0\n",
       "6    1\n",
       "7    1\n",
       "8    0\n",
       "Name: class, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-29T07:27:27.899234Z",
     "start_time": "2024-02-29T07:27:27.892248Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 调用决策树并进行训练\n",
    "clf = DecisionTreeClassifier()\n",
    "clf.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-29T07:27:28.348608Z",
     "start_time": "2024-02-29T07:27:28.330716Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看模型预测的准确率\n",
    "clf.score(X,y) # X -> model -> yhat --> y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于树模型来说，我们不仅需要看模型最终预测的结果，很多时候我们还想要看树模型分类过程中形成的树状结构。可以借助sklearn.tree模块下的plot_tree函数进行树状图的绘制："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-29T07:27:29.042522Z",
     "start_time": "2024-02-29T07:27:28.807431Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import tree    #导入tree模块\n",
    "tree.plot_tree(clf);        #绘制树状图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果你想，还可以对树状图进行美化以及将绘制好的决策树图片保存到本地："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-29T07:27:29.684973Z",
     "start_time": "2024-02-29T07:27:29.485555Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 对决策树进行美化并保存决策树图片到本地\n",
    "\n",
    "plt.figure(figsize=(5,5))      #设置画布大小（单位为英寸）\n",
    "tree.plot_tree(clf             #训练好的决策树评估器\n",
    "               ,node_ids=True  #显示节点id\n",
    "               ,filled=True    #给节点填充颜色\n",
    "               ,rounded=True   #节点方框变成圆角\n",
    "               ,fontsize=12    #节点中文本的字体大小\n",
    "              )\n",
    "\n",
    "plt.savefig(\"决策树.png\")      #保存图片一定要在plt.show()之前\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-29T07:27:29.723397Z",
     "start_time": "2024-02-29T07:27:29.721337Z"
    }
   },
   "outputs": [],
   "source": [
    "# ?plt.plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这个树状图如何解读呢？我们结合着原始数据集A来看："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><img src=\"https://typora-photo1220.oss-cn-beijing.aliyuncs.com/DataAnalysis/008i3skNly1gsk5h2sslqj30ve0j4abf.jpg\" alt=\"1\" style=\"zoom:30%;\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 每个节点中的信息如何解读？以node #0这个节点为例：\n",
    "\n",
    "  > 1. X[1]<=1.5 ：指的是按照特征中索引为1的那个特征进行分枝（也就是数据集A中的credict_rating列）\n",
    "  >\n",
    "  > 2. credict_rating列中的值，满足X[1]<=1.5条件的被分到node #1中，否则分到node #2中（满足条件的永远被分配到左边分枝）\n",
    "  > 3. gini=0.469：指的是node #0这个根节点中所有数据的基尼系数为0.469（之前我们算出$gini\\_A =0.46875$ ，plot_tree绘制树状图默认保留3位小数）\n",
    "  > 4. sample=8：指的是这个节点中共有8个样本\n",
    "  > 5. value=[5,3]：指的是这个节点中，标签为0的有5个样本，标签为1的有3个样本\n",
    "\n",
    "- sklearn中的决策树都是CART算法的优化版，所以它形成的树是二叉树\n",
    "\n",
    "- 如果对树状图设置了颜色填充，同一类别的叶子显示为同一色系，且叶子纯度越高颜色越深（节点的最终类别根据少数服从多数原则来判别）\n",
    "\n",
    "- 如果不加限制，决策树会无限生长，直到每一片叶子中只有一类标签，每一个叶子的基尼系数为0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 CART分类树评估器的参数详解\n",
    "\n",
    "*class* `sklearn.tree.DecisionTreeClassifier`(***, *criterion='gini'*, *splitter='best'*, *max_depth=None*, *min_samples_split=2*, *min_samples_leaf=1*, *min_weight_fraction_leaf=0.0*, *max_features=None*, *random_state=None*, *max_leaf_nodes=None*, *min_impurity_decrease=0.0*, *class_weight=None*, *ccp_alpha=0.0*)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-29T07:27:31.101525Z",
     "start_time": "2024-02-29T07:27:31.089395Z"
    },
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "?a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "实际上DecisionTreeClassifier评估器参数众多，并且大多和决策树的模型结构相关：\n",
    "\n",
    "|Name|Description|      \n",
    "|:--:|:--:| \n",
    "|criterion|规则评估指标或损失函数，默认基尼系数，可选信息熵| \n",
    "|splitter|树模型生长方式，默认以损失函数取值减少最快方式生长，可选随机根据某条件进行划分|\n",
    "|max_depth|树的最大生长深度，类似max_iter，即总共迭代几次| \n",
    "|min_samples_split|内部节点再划分所需最小样本数| \n",
    "|min_samples_leaf|叶节点包含最少样本数| \n",
    "|min_weight_fraction_leaf|叶节点所需最小权重和| \n",
    "|max_features|在进行切分时候最多带入多少个特征进行划分规则挑选|\n",
    "|random_state|随机数种子| \n",
    "|max_leaf_nodes|叶节点最大个数| \n",
    "|min_impurity_decrease|数据集再划分至少需要降低的损失值| \n",
    "|min_impurity_split|数据集再划分所需最低不纯度，将在0.25版本中移除| \n",
    "|class_weight|各类样本权重| \n",
    "|presort|已在0.24版本中移除| \n",
    "|ccp_alpha|在执行CART树原生原理中的剪枝流程时结构复杂度惩罚因子的系数，默认情况下不使用该方法进行剪枝| "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2.1 重要参数详解\n",
    "\n",
    "- **criterion**\n",
    "\n",
    "Criterion这个参数正是用来决定不纯度的计算方法的。sklearn提供了两种选择：\n",
    "\n",
    "1）输入”entropy“，使用**信息熵**（Entropy）\n",
    "\n",
    "2）输入”gini“，使用**基尼系数**（Gini Impurity）\n",
    "$$\n",
    "Entropy(t) = -\\sum_{i=0}^{c-1}p(i|t)log_2p(i|t)\\\\ Gini(t) = 1-\\sum_{i=0}^{c-1}{p(i|t)}^2\n",
    "$$\n",
    "其中t代表给定的节点，i代表标签的任意分类，$p(i|t)$ 代表标签分类i在节点t上所占的比例。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "比起基尼系数，信息熵对不纯度更加敏感，对不纯度的惩罚最强。但是**在实际使用中，信息熵和基尼系数的效果基本相同。**信息熵的计算比基尼系数缓慢一些，因为基尼系数的计算不涉及对数。一般情况下推荐使用基尼系数。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果一定要寻找二者在使用上的不同，一般认为在有些情况下，基尼系数更倾向于在数据集中分割出多数类，而信息熵则更倾向于生成出更加平衡的树。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-30T02:32:19.680938700Z",
     "start_time": "2024-05-30T02:32:17.750734300Z"
    }
   },
   "outputs": [],
   "source": [
    "# 尝试建立一棵稍微复杂一点的树\n",
    "\n",
    "# 导入相应的包\n",
    "import pandas as pd\n",
    "from sklearn import tree\n",
    "from sklearn.tree import DecisionTreeClassifier as DTC\n",
    "from sklearn.datasets import load_wine\n",
    "from sklearn.model_selection import train_test_split "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-30T02:33:06.899834Z",
     "start_time": "2024-05-30T02:33:06.855259300Z"
    }
   },
   "outputs": [],
   "source": [
    "# 导入数据集并探索\n",
    "wine = load_wine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": [],
    "ExecuteTime": {
     "end_time": "2024-05-30T02:33:09.001375100Z",
     "start_time": "2024-05-30T02:33:08.969478300Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names'])"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-30T02:33:11.784257100Z",
     "start_time": "2024-05-30T02:33:11.752657900Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "(178, 13)"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-30T02:33:13.372290500Z",
     "start_time": "2024-05-30T02:33:13.343268Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,\n       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n       2, 2])"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-30T02:33:29.245101100Z",
     "start_time": "2024-05-30T02:33:29.219859700Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "['alcohol',\n 'malic_acid',\n 'ash',\n 'alcalinity_of_ash',\n 'magnesium',\n 'total_phenols',\n 'flavanoids',\n 'nonflavanoid_phenols',\n 'proanthocyanins',\n 'color_intensity',\n 'hue',\n 'od280/od315_of_diluted_wines',\n 'proline']"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-30T02:34:22.574081400Z",
     "start_time": "2024-05-30T02:34:22.524794Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "array(['class_0', 'class_1', 'class_2'], dtype='<U7')"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-30T02:34:25.507719900Z",
     "start_time": "2024-05-30T02:34:25.469244500Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "     alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n0      14.23        1.71  2.43               15.6      127.0           2.80   \n1      13.20        1.78  2.14               11.2      100.0           2.65   \n2      13.16        2.36  2.67               18.6      101.0           2.80   \n3      14.37        1.95  2.50               16.8      113.0           3.85   \n4      13.24        2.59  2.87               21.0      118.0           2.80   \n..       ...         ...   ...                ...        ...            ...   \n173    13.71        5.65  2.45               20.5       95.0           1.68   \n174    13.40        3.91  2.48               23.0      102.0           1.80   \n175    13.27        4.28  2.26               20.0      120.0           1.59   \n176    13.17        2.59  2.37               20.0      120.0           1.65   \n177    14.13        4.10  2.74               24.5       96.0           2.05   \n\n     flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n0          3.06                  0.28             2.29             5.64  1.04   \n1          2.76                  0.26             1.28             4.38  1.05   \n2          3.24                  0.30             2.81             5.68  1.03   \n3          3.49                  0.24             2.18             7.80  0.86   \n4          2.69                  0.39             1.82             4.32  1.04   \n..          ...                   ...              ...              ...   ...   \n173        0.61                  0.52             1.06             7.70  0.64   \n174        0.75                  0.43             1.41             7.30  0.70   \n175        0.69                  0.43             1.35            10.20  0.59   \n176        0.68                  0.53             1.46             9.30  0.60   \n177        0.76                  0.56             1.35             9.20  0.61   \n\n     od280/od315_of_diluted_wines  proline  label  \n0                            3.92   1065.0      0  \n1                            3.40   1050.0      0  \n2                            3.17   1185.0      0  \n3                            3.45   1480.0      0  \n4                            2.93    735.0      0  \n..                            ...      ...    ...  \n173                          1.74    740.0      2  \n174                          1.56    750.0      2  \n175                          1.56    835.0      2  \n176                          1.62    840.0      2  \n177                          1.60    560.0      2  \n\n[178 rows x 14 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>alcohol</th>\n      <th>malic_acid</th>\n      <th>ash</th>\n      <th>alcalinity_of_ash</th>\n      <th>magnesium</th>\n      <th>total_phenols</th>\n      <th>flavanoids</th>\n      <th>nonflavanoid_phenols</th>\n      <th>proanthocyanins</th>\n      <th>color_intensity</th>\n      <th>hue</th>\n      <th>od280/od315_of_diluted_wines</th>\n      <th>proline</th>\n      <th>label</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>14.23</td>\n      <td>1.71</td>\n      <td>2.43</td>\n      <td>15.6</td>\n      <td>127.0</td>\n      <td>2.80</td>\n      <td>3.06</td>\n      <td>0.28</td>\n      <td>2.29</td>\n      <td>5.64</td>\n      <td>1.04</td>\n      <td>3.92</td>\n      <td>1065.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>13.20</td>\n      <td>1.78</td>\n      <td>2.14</td>\n      <td>11.2</td>\n      <td>100.0</td>\n      <td>2.65</td>\n      <td>2.76</td>\n      <td>0.26</td>\n      <td>1.28</td>\n      <td>4.38</td>\n      <td>1.05</td>\n      <td>3.40</td>\n      <td>1050.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>13.16</td>\n      <td>2.36</td>\n      <td>2.67</td>\n      <td>18.6</td>\n      <td>101.0</td>\n      <td>2.80</td>\n      <td>3.24</td>\n      <td>0.30</td>\n      <td>2.81</td>\n      <td>5.68</td>\n      <td>1.03</td>\n      <td>3.17</td>\n      <td>1185.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>14.37</td>\n      <td>1.95</td>\n      <td>2.50</td>\n      <td>16.8</td>\n      <td>113.0</td>\n      <td>3.85</td>\n      <td>3.49</td>\n      <td>0.24</td>\n      <td>2.18</td>\n      <td>7.80</td>\n      <td>0.86</td>\n      <td>3.45</td>\n      <td>1480.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>13.24</td>\n      <td>2.59</td>\n      <td>2.87</td>\n      <td>21.0</td>\n      <td>118.0</td>\n      <td>2.80</td>\n      <td>2.69</td>\n      <td>0.39</td>\n      <td>1.82</td>\n      <td>4.32</td>\n      <td>1.04</td>\n      <td>2.93</td>\n      <td>735.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>173</th>\n      <td>13.71</td>\n      <td>5.65</td>\n      <td>2.45</td>\n      <td>20.5</td>\n      <td>95.0</td>\n      <td>1.68</td>\n      <td>0.61</td>\n      <td>0.52</td>\n      <td>1.06</td>\n      <td>7.70</td>\n      <td>0.64</td>\n      <td>1.74</td>\n      <td>740.0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>174</th>\n      <td>13.40</td>\n      <td>3.91</td>\n      <td>2.48</td>\n      <td>23.0</td>\n      <td>102.0</td>\n      <td>1.80</td>\n      <td>0.75</td>\n      <td>0.43</td>\n      <td>1.41</td>\n      <td>7.30</td>\n      <td>0.70</td>\n      <td>1.56</td>\n      <td>750.0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>175</th>\n      <td>13.27</td>\n      <td>4.28</td>\n      <td>2.26</td>\n      <td>20.0</td>\n      <td>120.0</td>\n      <td>1.59</td>\n      <td>0.69</td>\n      <td>0.43</td>\n      <td>1.35</td>\n      <td>10.20</td>\n      <td>0.59</td>\n      <td>1.56</td>\n      <td>835.0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>176</th>\n      <td>13.17</td>\n      <td>2.59</td>\n      <td>2.37</td>\n      <td>20.0</td>\n      <td>120.0</td>\n      <td>1.65</td>\n      <td>0.68</td>\n      <td>0.53</td>\n      <td>1.46</td>\n      <td>9.30</td>\n      <td>0.60</td>\n      <td>1.62</td>\n      <td>840.0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>177</th>\n      <td>14.13</td>\n      <td>4.10</td>\n      <td>2.74</td>\n      <td>24.5</td>\n      <td>96.0</td>\n      <td>2.05</td>\n      <td>0.76</td>\n      <td>0.56</td>\n      <td>1.35</td>\n      <td>9.20</td>\n      <td>0.61</td>\n      <td>1.60</td>\n      <td>560.0</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n<p>178 rows × 14 columns</p>\n</div>"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 如果wine是一张表，应该长这样：\n",
    "pd.concat([pd.DataFrame(wine.data,columns=wine.feature_names),pd.DataFrame(wine.target,columns=[\"label\"])],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-30T02:35:20.488248800Z",
     "start_time": "2024-05-30T02:35:20.470275800Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "(178, 13)"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-30T02:35:22.466023800Z",
     "start_time": "2024-05-30T02:35:22.431543600Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "124.6"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "178*0.7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-30T02:35:24.429673Z",
     "start_time": "2024-05-30T02:35:24.384514100Z"
    }
   },
   "outputs": [],
   "source": [
    "# 切分训练集和测试集\n",
    "Xtrain, Xtest, Ytrain, Ytest = train_test_split(wine.data,wine.target,test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-30T02:35:28.032317800Z",
     "start_time": "2024-05-30T02:35:27.962386Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "array([1.371e+01, 1.860e+00, 2.360e+00, 1.660e+01, 1.010e+02, 2.610e+00,\n       2.880e+00, 2.700e-01, 1.690e+00, 3.800e+00, 1.110e+00, 4.000e+00,\n       1.035e+03])"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xtrain[0,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-30T02:35:30.323557600Z",
     "start_time": "2024-05-30T02:35:30.309096100Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "(178,)"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-30T02:35:32.399170Z",
     "start_time": "2024-05-30T02:35:32.365222300Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "(124, 13)"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xtrain.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-30T02:35:33.541755600Z",
     "start_time": "2024-05-30T02:35:33.523286500Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "(54, 13)"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xtest.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-30T02:35:34.795168400Z",
     "start_time": "2024-05-30T02:35:34.753153100Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "(54,)"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ytest.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-30T02:35:36.295135Z",
     "start_time": "2024-05-30T02:35:36.270733600Z"
    }
   },
   "outputs": [],
   "source": [
    "# 建立决策树模型\n",
    "clf = DTC()\n",
    "clf = clf.fit(Xtrain, Ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-30T02:35:42.430445Z",
     "start_time": "2024-05-30T02:35:41.184130300Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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wY8cOnDp1CioqKnjx4kW5eQMqG0QoOTkZHh4eAACxWAyRSCTxPjU1tVoVDqanpyMtLQ2DBw/GgwcPuOV6enrIzs7mvs/JyYGenl6l61m9enWFicfmzZvh4+ODH3/8Ef/+97/Rrl27KuPp0qULoqOj8eDBA9jZ2cHR0REtWrTgXnd3d+cGSyKE1D9KBgipZ25ubti+fTvS09NhZmaG7Oxs7Nu3D3fu3EFhYSG6d++Oj4f30NXVRUZGBgBw98uB0uTi8OHDaNu2LYD/HzWwTEFBAYYOHVouBqFQKFEU+LGEhAT8888/GD58OJ48eQKxWAwTExOIRCIsXboUYrEY7969w5MnT7i5BDIyMiTqAgAgNTUVX3/9NQDg6dOnmDBhAk6cOIGOHTvi9OnTYIzBw8OD+7T/4sULtGzZkhuJEChNcsrmJ2jZsiU32dHr16/RqlUrAKXzOHw8oiIhpP5QMkBIPZsyZQo6duyItWvXAgBat26Nnj17QigUomfPnhV+0p41axamTJmCQ4cOoUuXLtzy7du3w9PTk7utsGTJEolL8rW9MjBu3DiuVmD//v14/Pgxxo4dCwBYtGgRdwVi8+bNaNq0KQoLC+Ho6Ig7d+5IrOfKlSvc1wKBACdOnAAAHDp0CL/88guA0lsnRkZGAIDPP/8cX3zxhcSgQHFxcVi2bBlUVFRQWFiIH3/8EUDpBEr79u2DpqYmdHV1JSZkIoTULxqBkJBakrcR7yZPnownT55g3759EAgENXrvb7/9hoSEBCxYsKDOMRw5cqRO6/D398fu3buxevVqLoEB5O94ECJLKBkgpJboj49soeNBSO3R0wSEEEKIkqNkgBBCCFFyVEBISB2lpKTwHQIBHQdC6oKSAUJqSV9fH1paWnB3d+c7FPI/Wlpa0NfX5zsMQuQOFRASUgcZGRm1Gif/n3/+wX/+8x/8/fffWLhwIaZMmSIx8JAyYYwhKCgIW7duhZGREb799ttqBymqjL6+frmxEAgh1aNkgJBGFhISghkzZqB169Y4evQoBgwYwHdIMuHGjRtwcXHB69evsW/fPonHBgkhDYsKCAlpJGKxGAsXLsS4ceNgZ2eHP/74gxKBDwwcOBB//PEHbG1t4ezsjM8++wxisZjvsAhRCnRlgJBGcP/+fbi4uCAxMRGbN2+Gt7e30t4WqA5jDP7+/vD19YWpqSmOHj0KQ0NDvsMiRKHRlQFCGtjx48fRt29f5OTkICYmBj4+PpQIVKFJkyZYsGABYmJikJ2djb59++L48eN8h0WIQqNkgJAGkp+fDy8vL0yaNAnDhw/nRsgj0jE3N0dCQgKGDx+OSZMmwcvLC/n5+XyHRYhCotsEhDSAu3fvYtKkSfjrr7/w448/Ys6cOXQ1oJYYY9i5cycWLVoEIyMjHDt2DF27duU7LEIUCl0ZIKSeHTp0CObm5nj//j1u3LiBuXPnUiJQB02aNMG8efMQGxuLd+/eoW/fvjh8+DDfYRGiUCgZIKSe5OXlYfbs2XBzc4OTkxN+//139O7dm++wFEafPn0QHx+PMWPGYMqUKZg9ezbev3/Pd1iEKAS6TUBIPUhJScGkSZNw//59+Pv7Y/r06XQ1oIEwxrB3714sWLAAhoaGOHbsGIyMjPgOixC5RlcGCKmjgIAA9OvXD8XFxYiLi8OMGTMoEWhATZo0wcyZM3Hz5k0UFxejX79+OHDgAN9hESLXKBkgpJbevXsHT09PeHp6wsXFBXFxcTA2NuY7LKVhYmKCuLg4TJo0CdOmTcP06dPx7t07vsMiRC7RbQJCaiEpKQkTJ05ERkYGfv75Z0ydOpXvkJTagQMHMH/+fHTu3BnHjh2DiYkJ3yERIlfoygAhNcAYw+7du9G/f3+oqqoiPj6eEgEZ4OHhgd9//x0qKioYMGAA9uzZA/qcQ4j0KBkgREpv376Fu7s7Zs+ejalTp+LGjRvo0aMH32GR/zEyMsLNmzfh7u6OWbNmYerUqXj79i3fYREiF+g2ASFSuHXrFiZNmoSnT59i165dcHV15TskUoVDhw5h7ty5aNeuHY4dO0aPeBJSDboyQEgVGGPYsWMHBg0ahObNmyMhIYESATkwZcoUxMfHQ1NTEwMHDsTPP/9Mtw0IqQIlA4RU4vXr13BxcYGXlxdmzpyJ69ev0zC4cqRbt26IjY3FzJkzMX/+fEyePBmvX7/mOyxCZBIlA4T8z9u3b2Fra4ukpCT8/vvv6Nu3L0JDQ3H8+HFs374dGhoafIdIakhDQwPbt2/HsWPHcPHiRZibmyM+Ph6JiYmwtbWlmgJC/oeSAUL+Z9u2bYiJiUFwcDAsLS2hq6uLP/74AxMmTOA7NFJHEydOREJCAlq3bg1LS0uEhIQgJiYG/v7+fIdGiEygAkJCALx58wadO3eGrq4uHjx4gM8++wzfffcd1NXV+Q6N1COxWIwlS5Zg69at6NKlC3JycpCeno6WLVvyHRohvKIrA4QAWLJkCXJycvDo0SMYGxvj8OHDOHHiBN9hkXp24sQJHDlyBMbGxnj8+DGys7Px1Vdf8R0WIbyjKwOEANDT08OrV69gaGgIc3NzmJqaYs6cOfjkk0/4Do3UoxcvXmDXrl1ITExEfHw87t+/D11dXWRlZfEdGiG8omSAEAC5ublo2rQptLS0+A6FNKK8vDyUlJRAW1ub71AI4RUlA4QQQoiSa8Z3AKT+ZGRkIDMzk+8wFJa+vj46derEdxhESnQ+ND46R+QXJQMKIiMjA0ZGRsjLy+M7FIWlpaWFlJQU6uzkAJ0P/KBzRH5RMqAgMjMzkZeXh8DAQBgZGfEdjsJJSUmBu7s7MjMzqaOTA3Q+ND46R+QbJQMKxsjICH379uU7DEJkAp0PhEiHxhkghBBClBwlA4QQQoiSo2RASXXp0gUikQi5ubkAAAcHB7Rp0wbr1q3j2pw+fRoDBw6EjY0NRo0axQ3McuLECfTo0UOibX1btmwZOnfuDAcHB4nlgYGBsLCwgIWFBSIiIrjlfn5+sLKygrW1NRITE6uM/0OrV6+GkZERRCIRRCIRCgoKGuxnIorh43Pn9u3bGD58OOzs7DB9+nQAQGpqKgYNGlTu97c+nThxAkZGRlVOoFUWq0gkwtq1awEABQUFmDRpEqytrTFgwABcvny5wWIkcoQRhRAfH88AsPj4eKnaGxoaSnz/6NEjtm/fPvbNN99wy9LS0lhBQQFjjLHt27ezFStWcK993LYqb9++lardh548ecLu37/P7O3tuWXZ2dmsd+/e7P379+zly5esd+/erLi4mP39999MJBKxkpISlpKSwkQiUbXxl1m1ahU7ePBgtfHUdP8SfjXk8frw3BGLxczBwYHl5OSUa5eWlibx+1uVvLw8VlxcXKM4Xr58yd6/f1/uXK4s1jJnz55lnp6ejDHGMjIyWN++fWu03crQOSLf6MqAgrt+/TqGDBmCkpISnDx5Eu7u7hW269ChQ7llBgYGUFVVBQCoq6tDRUVF6u3m5uYiMDAQo0ePhq+vb43jbteuHZo2lfz1vHHjBmxsbKChoQF9fX20a9cO6enpiIiIwJgxY9CkSRP06NEDL1++RFFRkdTxb9y4EUKhEN9//32N4ySKS5pzJzY2Fi1atICHhwdEIhHOnTsn9fqLi4tx+fJleHp6Yvjw4RCLxTWKT19fv9pptRljsLW1xbBhw5CQkAAAMDQ0hFgsBmMM2dnZNOQ2AUBPEyg8CwsLDBkyBPPmzcPt27cRFhZW43U8e/YM/v7+OH/+fLVtL126hAMHDiAzMxNjxozB3r170aZNGwDAzZs3sWTJknLv8fHxkWqa4KysLOjo6HDf6+joICsrC1lZWWjXrh23vFWrVsjOzua2W1X8CxYswKpVqyAWi+Hk5IQ+ffrA1ta22liI4pPm3Hny5AkSEhJw69YtMMZgZWWFwYMHVzkL4p9//ondu3cjISEBIpEIS5cuRffu3QGUXsIfOnRoufcIhcJa3Za7ceMG9PX1kZSUhEmTJuHPP//Ev//9b+Tl5aFHjx7IycnB6dOna7xeongoGVACXl5eaN++PTZv3lzjMdizs7Mxfvx47Nq1S+IPbmWCgoJw//59LFiwAGPGjJHY3oABAxAZGVnT8Dl6enrIzs7mvs/JyYGenl655W/evOGShuri19PTAwBoaGhg/Pjx+P333ykZIJzqzh1dXV0MGjQIrVu3BgD06tULqampVT7OGB4ejgsXLmDu3LlwcXGR+L1UU1Or0znyMX19fQCAiYkJWrdujZcvXyI4OBgdOnTAqVOnkJ6eDmdnZ/zxxx/1tk0in+g2gRJYuHAhvv32W+zcuRMvXryQ+n25ublwcnLCmjVr0L9/f6neExAQgHPnziEnJwfOzs5wdXVFaGgogNIrA2XFTB/+k3aq4IEDByIqKgpisRivXr3CkydPYGBgAJFIhPPnz4Mxhnv37kFPTw/NmjWTKv6cnBwApZdTIyIiuE9ohADVnzuDBg1CamoqCgsLUVBQgJSUFHTu3LnKdfr4+OD27dvo1KkTFi5ciBEjRuCXX35BcXExCgoKKjxHVqxYUePYxWIx8vPzAQBPnz7Fy5cvueS37KqZjo4OVwhJlByvFQuk3lRWvHPw4EE2Z84cxhhjV69eZSNGjGDFxcXlCoumT5/OevbsyQwNDZmjoyNjrLS4rk2bNszGxobZ2NiwNWvWcO2lLSB88OABO3DgQI1/nh9++IFZWVkxXV1dZm9vz/7++2/GGGMBAQFs0KBBbNCgQezKlStc+w0bNjBLS0tmZWXFbt26VWX8v/76KxfTtGnT2KBBg9jAgQOZr69vpfFQcZR8qY/jJe25c/jwYWZlZcX69+/Pdu/ezS2XtoAwOzub7dq1i+Xl5dUovoiICGZvb880NTWZvb09O3bsGGOMsc8++4y9ePGCZWRkMDMzMyYUCtmgQYPY5cuXGWOM5ebmstGjR7PBgwezfv36saNHj9Zou5Whc0S+UTKgIGp6IgqFQmZjY1OrSv/jx48zc3Nztm3bthq/V15RRydfGvJ4SXPu3Lt3j1lZWTE3N7d6376sonNEvlHNgJKKioqq9XsnTJggVcEfIYpImnNHIBAgOjq6EaIhpH5QzQCR2q1bt+Dn51dlm0WLFuHly5e13sajR4/g4OAAa2vrSh9JnDZtGtq1a4dZs2Zxy65fvw5LS0vY2NjAzs4ODx48qHUMhEhDVs6HigYWSk9Ph46ODrf8zJkzAIAHDx5g8ODB3PKHDx/WOjaiYPi+NEHqh6JconN1dWVhYWHc1+Hh4eXaPH78mEVERLCZM2dyy548ecJyc3MZY4ydP3+eubu712tcirJ/lYWiHC9pzoeKBhaqrF7hiy++YPv372eMldZELF68uN5iVZR9rqzoygApp6SkBB4eHrC2toaPjw8MDAwAAJGRkdyncU9PT8yZMwejR49G3759ce/ePQCASCTC48ePa73thIQE2NnZAQDGjh0rMeRwmfbt25db1q5dOzRv3hxAzQdIIqQqsn4+sAoGFgJKh0m2traGu7s7d3XC2NiYe4Lm1atXNOAQ4VDNACnn9OnTUFdXR1RUFO7fv4/du3dX2K5Hjx7YtWsX9u/fjz179mDDhg0VtsvIyICHh0e55RMmTICPj4/EspKSEu7rskGFauLdu3f4+uuvsWPHjhq9j5DKyPr5UNHAQm3btsWDBw/QokUL7N27F76+vggICIC9vT2GDRuGPXv2ID8/Hzdv3qzJriAKjJIBUs7du3cxaNAgAKVDl5YNXPKxsmf3DQwM8Ntvv1W6vk6dOkk9kMqHQxCXDSokLbFYjPHjx2Pp0qXo3bu31O8jpCqyfj5UNLBQmzZtoK6uDgBwc3PDli1bAABfffUVvvnmG0yYMAFHjhzB0qVLKXEmAKiAkFSga9eu3CeGtLQ0ZGZmVtiuSZMm3NeMsUrXl5GRUeFAKv7+/uXampmZ4erVqwCAM2fOQCQSSRVzUVERJk+eDFdXV4wePVqq9xAiDVk+HyobWOj169dcm4iICHTr1o37vmzAoTZt2nC3DAihKwOkHCcnJwQHB8Pa2hqmpqZo27ZtndZXk09CGzZswIwZM1BQUIB+/fpxQwMvWrQIy5cvR5s2bbB06VJcunQJz549g4ODA4KDgxEcHIywsDBkZ2dj37596NmzJ3766ac6xU0IINvnQ35+PpycnNC8eXMUFRVhx44daNq0KSIiIrB27Vq0aNEC6urq2LVrFwBgxYoVmDt3Lpo1a4bCwkLs3LmzTj8LURxNWFUpLJEbCQkJMDc3R3x8fJXjokursLAQqqqqSE1NhYuLC+Lj4+shSvlV3/uXNCw6HxofnSPyja4MkAq5uLggKysLubm52Lx5M9/hEMIrOh+IoqNkgFQoODiY7xAIkRl0PhBFRwWEhBBCiJKjZIA0OIFA0Kjb27NnD1RVVbnvvby8uIrtTz/9FNu2bWvUeAipTGOdG2vXruXOAQMDA3zxxRcAgHHjxnHLW7dujbNnzzZKPET20G0ColDy8vIQHByMTp06ccs+fKqgZ8+eNMkSUTorV67EypUrAQBDhw6Fi4sLgP+//ZGfn48ePXpg6NChvMVI+EVXBpRYeno6rKysYGtrC5FIhOzsbFy7dg22trawtrbGmDFj8P79ewClw6p+/vnnGDZsGEQiEQ4dOoRhw4ahT58+uHv3LtfG29sbQ4YMgb29PV68eCGxvcLCQsyePRt2dnYQCoW4fv06AGDdunUYMGAA7OzsuMFRamvTpk1YsGCBxDPfZWJjY9GxY8c6PxpGFJ8inhsA8M8//+Dx48cYMGCAxPJTp05h2LBh3EBFRPlQMqDEwsPDMWTIEERERCAyMhKtW7eGubk5IiIiEBUVBSMjIxw7doxrb2Njg9DQUAgEAsTFxSE0NBS+vr7Yu3cv12bgwIG4fPky3Nzcys3otmfPHhgaGiI8PBwhISFYvHgxACAoKAgREREIDw/HokWLysX54aXMsn8fzlhY5tmzZ4iPj8fw4cMr/HkPHjyIqVOn1mZXESWjaOdGmaCgIEyZMqXccjo3CN0mUGKTJk3C+vXr4ebmhs6dO2P16tVITk7GihUrIBaL8fz5c7Rs2ZJrb25uDgDo0KEDDA0Nua/LRkgDAEtLS+7/kJAQie0lJiYiJiYGFy9eBABu9DN/f394e3ujqKgI8+bNg1AolHiftJXcq1evxooVKyp8raCgAOfPn8fGjRulWhdRbop2bpQJDAzE6dOnJZY9f/4c9+7dg5WVVY3WRRQLJQNKrGnTpli/fj0AYMaMGQgNDcXu3buxZs0aWFhYYMmSJRLDqn546b2yoVdjY2MhEAgQGxuL7t27S2zP2NgYAoEAn3/+OYDSP9AAYGFhAXt7e2RkZMDZ2bncgC7jxo3Dq1evJJYJBIJyE8akpqbi66+/BlA6NOuECRNw4sQJAMCFCxcwePBgbmZDQqqiaOcGANy6dQutWrXiZl0sc/jwYUyePLnCW2tEeVAyoMTOnTuHbdu2QUVFBerq6hAKhcjNzcXMmTPRo0cPtGzZUuLTjzQSEhIQEBCA4uJiHD58WOK12bNnw8fHhxtS1czMDFu2bIGzszPy8/ORn58Pb2/vcuuU9tPPlStXuK8FAgGXCACll0Hnzp1bo5+FKC9FOzeA0nPA3d29wuUfx0OUDw1HrCBkYShQkUiEwMBAdOjQgZftNyRZ2L9EerJ2vBT53Cgja/uc1AwVEBJCCCFKjm4TkHoj7UxshCgbOjeIrKMrAwTp6elwcHBotO2JRCJYWFggNTUVQGl1tY2NDfr37y/1vcuPRxkss3LlSqlGdQsLC4OFhQUsLCy4bebm5kIkEjX6iIlEtsji+XDmzBnu0cE+ffpwTy9UxtPTE2ZmZhCJRBg3blyVbW/fvs1t08rKCrdv3wYA7N+/H126dOG2m5GRAaB0GuRPP/0U0dHRdf3RiQyhKwOEF8ePH+fun0ZGRkJNTQ1v3rxB79694erqWuV7KxplEACePHmCe/fuVbvt4uJiLF68GOHh4dDS0oKFhQUcHR3RokULREZGUjJAGl1158OYMWMwZswYAMD69evRrFn1Xfe2bdvKPYpYkZ49e+K3335DkyZNEB4ejnXr1uH48eMAgJkzZ5Z7XHfdunV4/PhxTX9EIuPoyoCC8vX1xcmTJwEARUVF6NWrFwoLC7Fs2TLY2dmhb9++2LFjR7n3eXp6chl/ZGQkN4BJUlISHBwcYGdnh4kTJyIvL6/eYlVTUwMAvH37FsbGxtW2r2yUwarGGfhQamoqDAwMoKenB01NTVhaWiIuLq52wRO5oEjnQ1BQENzc3Kpdz+LFi2FtbY1Dhw5V2U5VVZU7l3JyctCrVy/utQMHDkAoFGL58uUoKSmpyY9B5AwlAwrK09MTAQEBAIDQ0FDY2dlBVVUVy5cvR3h4OK5fv47vv/8ehYWFUq3Py8sLe/fuRXh4OEQiEXbt2iXxekFBQbmR0EQikVR/nN+/f4/BgwejV69ecHJyqrJtZaMM3rlzBwCkSiaysrKgo6PDfa+jo4OsrKxq30fkl6KcD3FxcWjbti3at29f5To2bdqEmzdv4uzZs9i8eXO1V8yuX7+OQYMGYcGCBdz8BE5OTkhJScHVq1fxzz//cPuPKCa6TaCgTExM8PLlS7x48QIBAQFYunQpAGDHjh04deoUVFRU8OLFi3JjpFc2YEpycjI8PDwAAGKxGCKRSOJ9ampqtS6S0tTUxLVr15CZmYn+/ftj0qRJaNWqVYVtK/v0v3LlSvj7+0u1PT09PWRnZ3Pf5+TkQE9Pr1axE/mgKOfDgQMHpBo2WF9fHwDQunVrDBs2DLdu3ULXrl0rbW9hYYHY2FjExsZiwYIFuHnzpkTC7OrqitOnT2P69Om1+pmI7KNkQIG5ublh+/btSE9Ph5mZGbKzs7Fv3z7cuXMHhYWF6N69Oz4eZkJXV5crFPrw0rmJiQkOHz7MTfJTNkJamYKCggpnPBMKhVi3bl2lMRYUFKBZs2Zo2rQpmjdvDg0NDWhoaAAAMjIyytUFVDbK4P3797lLuE+fPoW3tze2b9+OV69eQU1NDdra2tw6BAIB0tPTkZ2dDS0tLcTExODbb7+temcSuSfv50NhYSHOnj0r8bv6/v175Obmok2bNhLrycnJQevWrVFYWIjo6GhulsInT57g008/hYqKCtc2Pz+f24aOjg60tLQk1gGUztXw8aiJRLFQMqDApkyZgo4dO2Lt2rUASj8l9OzZE0KhED179qzw0/CsWbMwZcoUHDp0CF26dOGWb9++HZ6entxl1CVLlkhcqq/tJ6GMjAzMmDEDKioqEIvFWLVqFdTV1VFYWAhHR0fu8n+ZykYZTExMlFi+fft2AMDmzZthbm4uUVGtoqKCTZs2YeTIkQBK7yfXdDQ5In/k+XwAgIsXL8LS0lIisY2OjsbZs2exdetWifW4uLggNzcXhYWFmDx5Mnr37g0AmDx5MkJCQrgrBwBw9uxZ+Pv7o2nT0rvGP/zwAwDAz88PV65cQbNmzdC1a1duvxEFxYhCiI+PZwBYfHw836FUy8XFhQmFQnbv3r1K20RHR7OtW7fWeVseHh4sPz9fqrZv375lNjY2zMbGptxr8rR/iXwdL2nOh8qsX7+e3b59W6q2BQUFbOrUqTXexseWL1/OjI2NWVxcnMRyedrnpDwajlhB0FCgDYv2r3yh49X4aJ/LN3qagBBCCFFylAwQQgghSo4KCBVMSkoK3yEoJNqv8omOW+OhfS3fKBlQEPr6+tDS0qpwvnJSP7S0tCSqsInsovOBH3SOyC8qIFQgGRkZyMzMbNBtvHnzBhMnTkS3bt2wdevWckMCN5b09HS4urpiwoQJ+OKLLxplm/r6+uXGPSCyqzHOhw9t2rQJJ0+exJEjR9C5c+dG2+6HGGNYsGAB7t27h+PHjzf6I7N0jsgvSgZIjXh4eODMmTNITk6udkjUhrZlyxb4+vri6tWrsLa25jUWotyuXbsGGxsbfP/991i0aBGvsTx+/BgmJiZwcnKiIYSJ1CgZIFI7c+YM18GUDcXKp+LiYtjY2ODZs2e4ffs2mjdvzndIRAm9e/cOvXr1Qrt27XD16lVu8B4+BQQEwNPTE2fOnMHo0aP5DofIAUoGiFSysrJgYmKCfv364cyZM7zdHvjYvXv30Lt3b8yaNavcKGyENIYFCxZgz549uHPnjsxMf80Yw+jRoxEfH4/k5GTo6uryHRKRcfynsEQuLFy4EGKxGDt37pSZRAAAunbtim+//Rbbtm2r9cQwhNRWREQE/P398d1338lMIgCUTrC0a9cu5OfnY+HChXyHQ+QAXRkg1QoODsb48eMRGBgo1Tzqja2kpAQikQiPHj1CYmKixNjthDSUt2/folevXujcuTPCw8Nl4vbAxwIDAzF16lQEBwfD2dmZ73CIDKNkgFTp5cuXMDY2hpWVFYKDg2XqqsCH7t+/j169esHT05ObpIiQhjR//nwcPHgQd+7cwb///W++w6kQYwzOzs64fv06kpOT6bE/UinZS2WJTPHx8UFxcTF27Nghs4kAABgaGmLjxo346aefEBYWxnc4RMFduXIFP//8MzZu3CiziQBQervg559/RlFREXx8fPgOh8gwujJAKnX8+HFMmjQJhw8fxuTJk/kOp1olJSWwt7fHgwcPkJiYSNMSkwbx5s0bmJqaQiAQ4PLlyzJ5e+Bjhw8fxpQpU3D8+HFMmDCB73CIDKJkgFToxYsXMDY2ho2NDY4fPy7TVwU+lJaWBlNTU7i5uWHnzp18h0MU0Jw5c3D48GEkJibCwMCA73CkwhjDhAkTcO3aNSQnJ+OTTz7hOyQiY2Q/pSWNjjEGLy8vAMBPP/0kN4kAAHTp0gWbNm3Crl27cOnSJb7DIQomNDQUv/zyCzZt2iQ3iQBQertgx44dAAAvLy/QZ0DyMboyQMo5cuQIXF1dcezYMUycOJHvcGqMMYahQ4fi77//RmJiIlq1asV3SEQBvH79GiYmJujRowcuXbokV0lymWPHjsHFxQVHjhyBi4sL3+EQGULJAJHw7NkzGBsbw8HBAUePHuU7nFp7+PAhTE1NMWnSJOzevZvvcIgCmDlzJo4fP46kpCS5Hn9/0qRJCA8PR3JyMv71r3/xHQ6REXSbgHAYY5g3bx6aNWsm94/nde7cGZs3b8aePXvw66+/8h0OkXMXLlzA3r178f3338t1IgAA27dvR9OmTTF//ny6XUA4dGWAcIKCguDu7q4wA5QwxjB8+HAkJSUhOTkZrVu35jskIoeys7NhYmKCXr164cKFC3J5e+BjJ0+exIQJExAUFIQpU6bwHQ6RAZQMEADA06dPYWxsjBEjRiAoKIjvcOrNo0ePYGJignHjxmHfvn18h0PkkKenJ06dOoWkpCR06NCB73DqzZQpU3Dx4kUkJyejbdu2fIdDeEa3CZRYcXExrly5AsYY5s6dCzU1NYWb7Kdjx4744YcfsH//fpw7d47vcIicOXv2LAICAvDDDz8oVCIAANu2bYOamhrmzp0LxhiuXLmC4uJivsMiPKErA0rs9OnTGDt2LHbu3Im5c+fi1KlTcHJy4jusescYg6OjI/744w8kJSXRDG5EKq9evYKxsTHMzc1x9uxZhbg98DFl6QNI9ejKgBK7d+8etLS08OWXX2Ly5MkK+0eybAa3vLw8fPbZZ3yHQ+TEwoULkZ+fj127dilkIgAAurq6mDx5MpYsWQJNTU2kpqbyHRLhCSUDSiwtLQ1NmzZFkyZNcPXqVTg6OuL9+/d8h9Ug2rdvj61btyIwMBCnT5/mOxwi406dOoWgoCBs3boV7dq14zucBvH+/Xs4OjpyU3+rqKggLS2N36AIbygZUGLR0dHIzc3F69evYW1tjdu3b0NTU5PvsBrM1KlTMXr0aMydOxdZWVl8h0NkVGZmJubOnYsxY8bA3d2d73AajKamJm7dugVra2u8fv0aubm5iIqK4jsswhNKBpTY27dv8a9//QtRUVE4evSoXA2vWhtNmjTBzp07UVBQgAULFgAonYOBymYIYwwvXrwAACxYsABFRUXYuXOnwt4eKNOlSxccO3YMUVFR+Ne//oXc3Fy+QyI8oWRAid2/fx/Pnj2DUCjkO5RG07ZtW2zbtg2HDx/GyZMn0b17d5w4cYLvsAjPTpw4wf0uHDlyBP7+/vj000/5DqvRCIVCPHv2jGoGlBglA0pM0T/1VGbUqFFwcnLC/Pnzoa6ujsTERL5DIjy7c+cONDQ04OXlhbFjx2LEiBF8h8QLZe0TCNCM7wBkQUZGBjIzM/kOQ2Hp6+vLzBCuJSUl6NGjB9q3bw+xWAxVVVUqmiJIS0tDYWEhCgsL8fDhQ/To0QP//PMPmjaVnc9L1E81LFnqp/ig9MlARkYGjIyMkJeXx3coCktLSwspKSkycaI1bdoUx48fx2effYY3b94AAOLi4niOivAtLi5Ooqj0+PHjMpcIUD/VsGSpn+KD0icDmZmZyMvLQ2BgIIyMjPgOR+GkpKTA3d0dmZmZMnOSWVtbIy4uDgEBAfD29sajR4/4Donw7PHjx9DQ0MBPP/0EDw8PqKio8B2SBOqnGpYs9lONTemTgTJGRkbo27cv32GQRqKiooIZM2Zg/PjxePLkCd/hEJ7FxcWhffv2aNWqFd+hVIn6KdJQKBkgSq1Vq1Yy/weANLyePXvyHQIhvJKdm2KEEEII4QVdGSAyi6qn60ddqqTpGDQsZa9gJ7KDkoEG1qVLF3Tu3Bnnzp3D8+fPMX36dDDGwBjDDz/8gH79+iE1NRXu7u7Q1tbGlStXGiSO//73vwgNDUVxcTEMDQ2xZ88eqKqqSrQJCwvDihUrAJRO0uLq6togsUiDqqfrT22rpOkYNLzGqGD/sA/S1taGg4MDbt++jc8++4w730+fPo3169dDQ0MD2traOHDgAPT09HDixAmsWLEC7u7uXNv6VFBQgKFDh3Lfx8bGIi4uDp988gmcnZ2hpqaG9+/fY/369bC3t8euXbtw6NAhAMCzZ89gbGyMkydPSqzz0aNHmD59OsRiMQYOHIhNmzbVe9yKiJKBBqaiosJNBCIWixESEgI9PT38+eefmDt3LqKioiAQCHDkyBHMmjVLqnW+f/8e6urqNXr06csvv8Ty5csBAB4eHrh06RJGjRrFvV5cXIzFixcjPDwcWlpasLCwgKOjI1q0aCH9D1uPqHq6ftSlSpqOQcNqrAr2D/sgANi/fz+uXLmCx48fc8t69+6N6OhoqKqq4qeffsIPP/yAb775BhMmTEBubq5E26rk5uZCW1tb6tjU1NS42B4/fozhw4fD1NQUxcXFiIqKgoqKCh48eAAXFxfExcVhzpw5mDNnDgBgzpw5cHBwKLfOr776CsuWLYOdnR2mTJmCiIgI2NraSh2TsqJkoJ5cv34dK1euRGhoKEJCQhASEoLAwECJNnp6etzX6urqNXp8qbi4GOHh4QgKCkJaWhouXrxYo0mF1NTUAJQOulNUVASBQCDxempqKgwMDLgYLS0tERcXBzs7O6m30RCoepp/dAzkgzR9EAB06NCh3LIP5yWpad+Um5uLU6dO4ejRo2jfvj1+/vnnWsUfGBgINzc3AJDYfk5ODnr16iXRNj8/H5cuXcLWrVvLrSchIYHrt8aOHUvJgJQoGagnFhYWGDJkCObNm4fbt28jLCys0rbFxcVYuHAh/vOf/1S73j///BO7d+9GQkICRCIRli5diu7duwMof4mtjFAoxLp168otX7VqFYKCgtCtWzd07NhR4rWsrCzo6Ohw3+vo6NDMfoTIkZr0QZV59uwZ/P39cf78+WrbXrp0CQcOHEBmZibGjBmDvXv3ok2bNgCAmzdvYsmSJeXe4+PjgwkTJlS4vqCgIIntpqWlYerUqfj777+xb98+ibZnz56Fg4MDNDQ0yq2npKSE+5r6MenR0wT1yMvLC0ePHsXs2bMrvVTGGMOMGTPg6OiI4cOHV7vO8PBwXLhwAU5OTpgzZw6XCAD/f4nt438VJQIAsGbNGty7dw8GBgbYv3+/xGt6enrIzs7mvs/JyZG4kqGobt26BT8/vyrbLFq0CC9fvqz1Nh49egQHBwdYW1vD19e3wjZhYWGwsLCAhYUFDh8+XOttyTM6FnUnTR9UmezsbIwfPx67du1Cu3btqm0fFBSE+/fvw8PDAx4eHlwiAAADBgyosG+qLBGIj48vV0zZpUsXREdH48aNG/Dx8ZFof+DAAUydOrXCdX14+1RZ+rH6QMlAPVq4cCG+/fZb7Ny5k5sO9WMLFiyAQCDA/PnzpVqnj48Pbt++jU6dOmHhwoUYMWIEfvnlFxQXF6OgoAAikajcv4oKffLz8wGUTkTSqlUraGlpSbwuEAiQnp6O7OxsiMVixMTEoF+/fjXcA/KnT58++PLLL6ts88MPP0h0dDVVdg8zKioK//zzDyIiIiReL6vXOHfuHMLDw/Hdd9/h7du3td6evKJjUXfS9EEVyc3NhZOTE9asWYP+/ftL9Z6AgACcO3cOOTk5cHZ2hqurK0JDQwGUXhmoqG+qbIbQgwcPSvxxF4vF3NctW7aUSGxevnyJlJQUDB48uMJ1mZmZ4erVqwCAM2fOQCQSSfXzKDu6TVBPAgMDoaqqCi8vL5iYmMDT0xPnzp2TaBMZGYldu3bB0tISYWFh0NXVRXBwcLXrVldXx/jx4zF+/Hjk5OTg+PHjKCgogKampkRhUFW8vLzw4MEDFBcXo1u3bli7di2A0k9ay5cvR5s2bbBp0yaMHDkSAODr64uWLVvWbCfIsJKSEnh6eiItLQ29e/fGuXPnkJ6ejsjISAQGBmL37t3w9PSEmpoanj59iidPnuDo0aPo2rUrRCIRAgMDK7zXKo3q7mHKar1GQ6Fj0TCk6YMAYMaMGbhx4wbEYjFu3LiBs2fPYtOmTfjrr7+wbt06rFu3DnZ2dli5cmW129TT04OXlxe8vLyQlpaG6OhoAP9/ZUAaRUVFOHXqFNcnAaUjQi5btgwqKiooLCzEjz/+yL125MgRTJo0SWKGxf3796N9+/YYMmQINmzYgBkzZqCgoAD9+vWjegEpUTJQT9zd3eHu7g4AGDx4MJe1tm3bFiKRCOfOnYNIJEJBQUG596ampsLT01OiiKcyrVu3xuzZs2sc3969eytc/sMPP3BfDxkyBEOGDKnxuuXB6dOnoa6ujqioKNy/fx+7d++usF2PHj2wa9cu7N+/H3v27MGGDRsqbJeRkQEPD49yyydMmFDukmZ19zCVrV6DjkXDkKYP0tbWrrAvWL16NVavXl1u+YkTJ+Dv7w9PT89qt9+lSxd06dKlxnE3a9YM6enpEsuEQiGuXbtWYfsFCxaUW/ZhfJ07d65VvYSyo2SggUVFRVXbRiAQcBk1aRh3797FoEGDAACGhobQ19evsF3ZJVIDAwP89ttvla6vU6dOUn/yqe4eprLVa9CxaFzS9EGVmTBhQqX3+YlioZqBBiQPBVFln4TKHkG6ffs2LC0tYWNjAysrK9y+fbvWscmSrl274ubNmwBKq5QrG1Xvw0uPjLFK15eRkVHhPVF/f/9ybau7h6ls9Rp0LGSTLPRXOTk5sLe3h1AoxKBBg/Drr79yrzk4OKBNmzaVFkiTuqErAw2oT58+6NOnT5VtPrxMXxvVDbBR3WBC69atg1Ao5Nr37NkTv/32G5o0aYLw8HCsW7cOx48fr1OMssDJyQnBwcGwtraGqakp2rZtW6f11eTTaGX3MJWlXuNjdCxkkyz0V+rq6ggICECHDh2QmZkJKysrjBgxAkDFgyWResSUXHx8PAPA4uPja72O4uJiNnXqVCYUCpm3tzfr3LkzY4yxiIgINnPmTMYYY9OmTWOzZ89mjo6OzMzMjN29e5cxxpiNjQ179OhRrbfdvXt37uujR4+yr7/+WuL1v/76i40ZM4b7fv78+SwsLIwxxti9e/fY1KlT2apVq9jBgwfLrfvkyZNs7dq1tY6Nsdrv3/o4Lh8rKChgjJX+3H379q239cqyuuzHhjgGZZTxWHysJvu3Po+FLPdXH8rNzZVozxhj+/btY998802tt1+Zhvxdlxd0ZaAeyGtB1MqVK/HNN9/g4MGDEu+5fv06Pv/8czx69Eiqpx3khYuLC7KyspCbm4vNmzfzHY5So2PBH1nurz60aNEiLF68WJofidQDSgbqgTwWRMXExEBPTw+Ghobl1mlhYYHY2FjExsZiwYIF3P1deadIiY28o2PBH1nur8p8/fXX0NXV5eYhIA2PCgjrgTwWRP3++++4c+cOhg8fjsDAQPj5+eH69evc4ERAaeb+8eBEpNTHczs0lJ07d2LAgAEYPHgwXF1dJQZjUWaNtf/37t0LoVCIwYMHY/To0Xjz5g2A0kfZzMzMIBKJMG7cuEaJpb7Icn8FAH5+fvjnn3/w3Xff1eTHInVEVwbqgTwWRC1cuBALFy4EUPqMsUAggIWFBY4fPw5/f38ug69rwRCpG3t7e8yePRtNmzbFkiVLEBgYiJkzZ/IdltJwd3fHjBkzAJTeVjt48CC8vb0BANu2bZMovpUXstxf5ebm4quvvoKVlRWXKFy6dAlqamoVDpZE6hHfRQt8q6/CESqIqlhjFxCmpaUxS0tLJhKJmI2NDXv16hW7evUqE4lETCgUstGjR7O8vDzGWGkx1KJFi9jQoUOZjY0NCwoKYkOHDmW9e/dmf//9N9fGy8uLOTg4MDs7O/b8+XPGGGOGhoaMsdLjPmvWLGZra8usrKxYTEwMY4yxb775hvXv35/Z2tqyzZs31+hnqMyKFSvYvn37avSexi4gVOT9//nnn7OIiAjGWGmBXf/+/ZlQKGRBQUG1Wh9fBYSMUX/1MSogpALCekMFUbIhPDwcQ4YM4UZTY4zB3NycG4P+q6++wrFjxzBt2jQAgI2NDb7//nvMmjULcXFxCA0NRWBgIPbu3csVTA0cOBDbt2/H3r174efnJ/Es9p49e2BoaIhffvkFL1++xJgxY3D9+nUEBQXh999/R/PmzSWKpsqMGzcOr169klgmEAgqLeZKSUnBr7/+yl1ilVWKuP937NiB7du3Q0tLC0uXLgUAbNq0Cfr6+txz8f3790fXrl3rvgMbCfVX5GOUDNQTKoiSDZMmTcL69evh5uaGzp07Y/Xq1UhOTsaKFSsgFovx/PlziefGzc3NAZTO8V5WTNmhQweJP7qWlpbc/yEhIRLbS0xMRExMDC5evAigtCgKAPz9/eHt7Y2ioiLMmzev3OXkmvy+pKenY9q0aTh27BiaN28u9fv4oIj7f/78+Zg/fz42bNgAPz8/bNy4kSu6a926NYYNG4Zbt27JVTJA/RX5GCUDMk4gECA1NbXRtrdnzx7MmzcPhYWFAErrCY4ePYp//etfAP7//p2satq0KdavXw+gdEKW0NBQ7N69G2vWrIGFhQWWLFkiUQz1YZFUZQVTsbGxEAgEiI2NlZhCGgCMjY0hEAjw+eefAwA394SFhQXs7e2RkZEBZ2dnxMfHS7xP2k+mz549w8SJE7Fnzx78+9//rvH+aGyKtv/z8/OhoaEBoLSgtqzANicnB61bt0ZhYSGio6Ph4uJSg72kWGShj/r111/RrFkzmJub48cff5T4XSLSoWSAcPLy8hAcHCwxpzgALF++nJsARdadO3cO27Ztg4qKCtTV1SEUCpGbm4uZM2eiR48eaNmyZY1HlEtISEBAQACKi4vLDec8e/Zs+Pj4cIVQZmZm2LJlC5ydnZGfn4/8/Hyu4OxD0n4y+89//oPnz59zxZ5TpkyR6cetFG3/r127FjExMQAAXV1dbpIfFxcX5ObmorCwEJMnT0bv3r1r9DOR2qmoj5oyZQp3W2rSpEkIDw+Hvb09TxHKMT4LFmQBFar9vzVr1rBff/2V2yZjjK1atYqZmpoyKysrtmXLlhqvU5ZGIKyNuo64xjdZHYFQWvK+/6vS0AWEytJHfcjd3Z0r8qwJWfhd5xuNM1BLZYVSERERiIyMROvWrblCqaioKBgZGeHYsWNcexsbG4SGhkIgEHCFUr6+vhLTiQ4cOBCXL1+Gm5tbuQlDygqlwsPDERISwo3MFRQUhIiICISHh2PRokXl4hw3bly5539nzZpVrt2zZ88QHx+P4cOHSyxfsGABbt++jStXruDixYtcIRghRLYpSx9VJjIyEo8fP+ambiY1Q7cJaknRCqVWr16NFStWlFteNkKYhoYGxo8fj99//11iYhFFJ+3z06Rh0P6vPWXpo4DSW0lLly7FuXPnJEY5JNKjZKCWFK1QKjU1FV9//TUA4OnTp5gwYQJOnDjBFUoxxhAREQFXV9ea7ShCCC+UpY9KSUnBnDlzcOrUqUqHNybVo2SglhStUOrKlSvc1wKBACdOnABQOjLY33//DcYYrK2tMWbMmBr9THxJT0/HrFmzJH6uhiQSiSAWi3Hw4EHo6+tj/PjxEIvFKCoqwqpVq7hpWCvCGMMXX3yBuLg4qKurY//+/ejQoUOl7R89eoTp06dDLBZj4MCB2LRpEwBg1qxZOHHiBJKSkqp8f2Pgc/8LBAKEhYVxnyIXLlxYZRJb0/3v4+ODxMREvH//HiKRCBs3bgQAdOnSBZ07dwYA2NnZYeXKlUhNTYW7uzu0tbUbbV+UUZY+av78+Xjz5g1X5Lx48WK56adkCn/lCrJBVgpHFLVQiq8CwrS0NGZvb1+r99bGh8cvLy+P+/rly5esW7duVb43NDSUTZ06lfvaw8Ojyvaurq7cNNSurq4sPDy8wjgY46+AkM/9X1RUxHr16sUyMzNZXl4e6927N3vz5k2l763p/heLxdzXgwcPZklJSYwxVmlRW2X7gs8RCGtDUfsoxmRj//KNbq4QueHr64uTJ08CAIqKitCrVy8UFhZi2bJlsLOzQ9++fbFjx45y7/P09ER0dDSA0nvQZcVJSUlJcHBwgJ2dHSZOnIi8vLx6iVNTU5P7ZKmpqVntM88RERFwdnYGAAwZMqTaWSITEhJgZ2cHABg7dmyjFXXKy/5PTU2FgYEB9PT0oKmpCUtLS8TFxVXavqb7v2ycjYKCAmhpaaFdu3YASq8w2NraYtiwYUhISKiXn4WQxkK3CWQEFUpVz9PTE8uWLcP48eMRGhoKOzs7qKqqYvny5WjevDnEYjFMTU0rrESuiJeXFwIDA9GpUyds374du3btkqh2LigowNChQ8u9TygUYt26dVJtQ5o52bOysqCjowOg9F5tcXFxle1rMid8fZKX/f/h/gSq30c13f8AMHfuXJw/fx7Dhg1Dq1atAAA3btyAvr4+kpKSMGnSJPz555/VrkeeUB+l2CgZIHLDxMQEL1++xIsXLxAQEMCNE79jxw6cOnUKKioqePHiBV68eCHxvsqKoZKTk+Hh4QEAEIvF5aZTVVNTq1MHKO2c7Hp6esjOzubia9as6tNS2jnh65u87P8P9ydQ/T6q6f4HSqeWLiwsxLhx43Dx4kWMHDmSG6LYxMQErVu3xsuXL9GmTZsax08IH+g2QR2lp6fDwcGh0bYnEolgYWHBDf85bdo0tGvXTupPY0Dp88Cqqqrllq9cuVKqeeLDwsJgYWEBCwsLrogoNzcXIpGoweeZd3Nzw/bt25Geng4zMzNkZ2dj3759uHr1KkJDQ9GqVatyc6/r6uoiIyMDACQuF5uYmODw4cOIjIzE9evXsWbNGon3FRQUVDhPe2WPN32osjnZy+L4kEgkwpkzZwCUPhver18/AMD79+/x8uXLcu2lmRO+ocjD/hcIBEhPT0d2djbEYjFiYmK4fVof+79sSGJVVVVoa2tDS0sLYrGYW/706VO8fPlSpirb+e6nLC0tYWNjg/79+3N9RtnvrkgkQp8+fbhHGyvj6ekJMzMziEQijBs3rtoY/Pz8YGVlBWtrayQmJgIAoqOj0adPnxr1l8qCrgzIoePHj3P3pNevX4/p06cjMDBQqvdWNuTwkydPcO/evWrfX1xcjMWLFyM8PBxaWlqwsLCAo6MjWrRogcjIyAZPBqZMmYKOHTti7dq1AEoniunZsyeEQiF69uxZYQc8a9YsTJkyBYcOHUKXLl245du3b4enpyc3xvmSJUskBjSp7SfTtLS0SudkHzZsGFJSUiTaDx06FL/++iusra2hrq6Offv2ASjtuM6ePYutW7dKtK9sTvjGIA/7X0VFBZs2bcLIkSMBlNY6lFXN18f+Hz9+PN69e4eCggIMHjwYIpEIjx49gpOTE5o3b46ioiLs2LFD6Z93/7CfioyMhJqaGt68eYPevXvD1dUVY8aM4ar+169fL9UVmW3btpUbp6Aid+/exYULFxAdHY2///4b8+fPR0REBIRCIX744Qep+0ulwmPxokyoqIr0iy++YCdOnGCMMVZYWMhMTU1ZQUEBW7p0KbO1tWVmZmbsp59+YoxJVgpPmzaNRUVFMcYYi4iIYDNnzmSMMZaYmMjs7e2Zra0tmzBhAnv37l2t462oovfDbVWnsuE8Z82axZKSkiqtiC7z119/sTFjxnDfz58/n6tsZ6x8RbW8D0csLRcXFyYUCtm9e/cqbfP48WO2ePFiqde5fv16dvv2banbz5w5k3Xv3p09ffqUWybvwxFLSxb2f0Xu3bvHrKysmJubW7nX6vo0gbz1U4yVHoNRo0aVW96zZ0/2+PHjKtc5bdo01r9/fyYUCllQUFCVbX/++WeJ4dONjY1ZYWEhY6zi/lKeftcbCl0ZqIC8FErVVNlwnitXrpRYfufOHQClg4ZUp6bFWcriyJEj1bZp3759jeaOL7snL62PB2lRJrKw/ysiEAi4Jynqmzz1U+/fv8ewYcOQnJyMDRs2SLwWFxeHtm3bon379lWuY9OmTdDX10dOTg7s7e3Rv3//SqeNzsrK4p7yAIBWrVohOzubajiqQMlABeSlUKqmKhvOc+XKlfD395dqHTUtziKENAx56qc0NTVx7do1ZGZmon///pg0aRL3FMaBAwcwderUatdRVqDZunVrDBs2DLdu3ao0Gfi4n3rz5o3EhxhSHiUDlaisUOrOnTsoLCxE9+7da1wo1bZtWwD/P0xnmYa4MpCRkVGuLqCy4Tzv37/PfXp4+vQpvL29sX37drx69QpqamrQ1tbm1vFhcZaWlhZiYmLw7bff1ipGQkjdyEM/VVBQgGbNmqFp06Zo3rw5NDQ0oKGhAQAoLCzE2bNnJfqQ9+/fIzc3t9yn+LKh0QsLCxEdHQ0XFxcApfVOn376KVRUVLi2IpEI3t7eWLRoEVJTU6GnpydVTYIyo71TCXkolAJKL2VeunQJz549g4ODA4KDg6GpqQlHR0fu8n+ZyobzLKu0LVu+fft2AMDmzZthbm4uUblbVXFWQ/i42IvUTH3sPzoGDaM+9qs89FMZGRmYMWMGVFRUIBaLsWrVKqirqwMALl68CEtLS4kPHJUVbrq4uCA3NxeFhYWYPHkyevfuDQCYPHkyQkJCuCsHANC9e3cMGTIEQqEQTZo04fo0UgV+Sxb4J2+FI9IUSkVHR7OtW7fWeVseHh4sPz9fqrZv375lNjY2zMbGRmJ5bffvw4cPmZaWFgNA/+r4T0tLiz18+LBG+5+OgWwdG0XspypTk8LNgoICbihpaURFRbGBAweyr776SmK5vO3fhtCEsY+uISmZhIQEmJubIz4+Hn379uU7HIVTl/2bkZGBzMzMBopMeejr65e7ZSQtOgYNS9pjQ/1Uw6L9S7cJiAzr1KlTrf+IkfpBx4AQ5aDco2IQQgghhK4MlKEiqYZB+5WQ+kPnU8Og/UrJAPT19aGlpQV3d3e+Q1FYWlpaEpW+hJCaoX6q4Sl7P6X0BYRA4xVJrVmzBleuXMGxY8e4Z3n58NVXXyEuLg7Hjx9vlAGD6lLARggp1Rj9VGZmJiZOnIiBAweWGymwMT19+hSTJk3CkCFDyo2Y2lCUvZ+iZKCRXLhwAaNGjcLu3bsxc+ZMXmN5+fIljI2NYWVlheDgYIkRyQghyokxBmdnZ1y/fh3Jycm8f0revXs3Zs+ejfPnz3PjmpCGQ8lAI8jOzoaJiQl69eqFCxcuyMQf3+DgYIwfPx5BQUGYMmUK3+EQQngWFBQEd3d3BAcHw9nZme9wwBjDiBEjkJiYiKSkJBpOuIFRMtAIpk2bhtOnTyMpKYmb0lMWTJkyBRcvXkRycjKvty0IIfz6559/YGJighEjRiAoKIjvcDiPHz+GiYkJxo4di/379/MdjkKjRwsb2NmzZ3HgwAH88MMPMpUIAKVzg6upqWHu3Lnlxi8nhCgHxhjmzp0LdXX1ckMA861Dhw74/vvvERAQgHPnzvEdjkKjKwMN6NWrVzA2Noa5uTnOnj0rE7cHPnb69GmMHTtW6pnDCCGKJSAgAJ6enjh9+jTGjBnDdzjlMMbg6OiIP/74A0lJSdDV1eU7JIVEyUADcnd3x/nz55GcnCwxt7asmTp1Ks6dO4ekpKRq5xQnhCiOJ0+ewNjYGGPGjMGBAwf4DqdST548gYmJCRwdHXHw4EG+w1FIdJuggZw6dQpBQUHYtm2bTCcCAPDjjz9CU1MTc+bModsFhCgJxhhmz54NLS0t/Pjjj3yHU6X27dtj69atCAwMxOnTp/kORyHRlYEGkJmZCWNjYwwaNAinTp2SydsDHzt37hxGjx6NvXv3Yvr06XyHQwhpYHv37sXMmTNx7tw5jBo1iu9wqsUYg5OTE27evInk5ORGGSNFmVAy0ABcXV1x6dIlJCcn49NPP+U7HKl5enoiJCQESUlJ6NixI9/hEEIaSEZGBkxNTTF+/Hjs3buX73Ck9vTpUxgbG2P48OE4dOgQ3+EoFEoG6tmJEycwceJEHDp0CK6urnyHUyM5OTkwNjaGqakpfv31V7m4okEIqRnGGIYNG4Y///wTSUlJaN26Nd8h1cihQ4fg5uaGEydOYPz48XyHozAoGahHZSP7WVtb48SJE3L5x/TXX3/FyJEj8csvv2DWrFl8h0MIqWe//PIL5syZg19//RXDhw/nO5waY4xh/PjxiI6ORnJyMtq0acN3SAqBkoF6NGnSJISHhyM5ORn/+te/+A6n1mbOnInjx48jMTERnTt35jscQkg9efjwIUxMTDB58mT88ssvfIdTa8+fP4exsTHs7e1x9OhRvsNRCJQM1JNjx47BxcUFR48exaRJk/gOp05ev34NExMTdO/eHZcvX5bLKxyEEEklJSUYMmQI7t27h6SkJLRs2ZLvkOrk6NGjmDx5skL0ubKAkoF6UJal2tnZ4dixY3yHUy8uXbqEYcOGYceOHZg3bx7f4RBC6mjHjh3w8vLC5cuX4eDgwHc4dcYYw6RJkxARESH3V2NlASUDdaTI96/mzp2LoKAgJCYmokuXLnyHQwippbS0NJiamsLd3R0///wz3+HUm7I6LaFQiJMnT9JVzDqgQYfq6PDhwwgJCcGOHTsUKhEAAD8/P+jp6WHmzJkoKSnhOxxCSA2IxWIcOXIEJSUlmDFjBvT19eHn58d3WPWqTZs2+OmnnxASEoIjR47wHY5coysDdVD2zOuwYcNw+PBhvsNpEFeuXMGQIUPg7+8Pb29vvsMhhEip7DFnPz8/fPnllwgLC4OdnR3fYTWIyZMn4/Lly3I3tossoSsDtVQ205eamhr8/f35DqfBODg4YP78+ViyZAnu37/PdziEECk9ePAA2traWLlyJWbMmIHXr1/zHVKD8ff3R7NmzTBv3jwaUr2WKBmopcDAQJw9exY7d+5U+GExN27ciE8++QQzZsyg2wWEyIkHDx6guLgYmpqaOHnyJObOnYv379/zHVaD0NfXx88//4zTp08jKCiI73DkEiUDtfDPP/9g4cKFcHNzg5OTE9/hNDhtbW3s27cP165dU+irIIQokqtXr+L9+/fIycmBm5sbkpOToampyXdYDcbZ2RlTpkzBggUL8M8///AdjtyhmgEpFRcX4/79++jatStGjx6N+Ph4JCcnK9Xc2gsWLMCePXtw+/Zt6OvrIz8/H23btuU7LEJIBXr16oX8/HycOnUKPXv25DucRpGVlQUTExP069cPZ86cwb1792BoaAgVFRW+Q5N5lAxI6eDBg/D29saPP/6IGTNm4MyZMxg9ejTfYTWqd+/eoVevXmjbti0GDBiA33//HdeuXeM7LEII4Zw5cwZOTk7Yt28fFi5ciJ9++gnu7u58hyXzmvEdgLxISUlBixYtsGjRIri7u6NXr158h9ToioqK8P3332Ps2LFo0aIFUlJS+A6JEEIk9O7dG25ubli0aBG0tbWpn5ISJQNSSk9Px/v379GsWTNcv34dgwYNwpMnT9C0qfKUXWzatAl+fn4wNzfHlStXUFRUhNzcXGhra/MdGiFSycjIQGZmJt9hyD19fX106tSJ7zDKKSkpwcCBA9GiRQuoqKggPz8f6enpfIclFygZkNKNGzeQnZ0NAOjbty+2bNmiVIkAAPznP/9B06ZN4efnh6KiIgDA/fv30bt3b54jI6R6GRkZMDIyQl5eHt+hyD0tLS2kpKTIXELQtGlTXL58GYsXL0ZqaiqA0r6bVI9qBqRUlmkGBgZi1KhRSj3s5aNHjzBr1ixcunSJ5hQnciMhIQHm5uYIDAyEkZER3+HIrZSUFLi7uyM+Ph59+/blO5wKMcZw/vx5uLu7o7i4GG/fvuU7JJlHVwaklJ6ejpYtW0JVVZXvUHjXsWNHhIaG4vnz5zQ5CJE7RkZGMvtHjNSPJk2awNHRES9fvsSbN2/4DkcuUDIgJUUfWKg2KBEghMgyVVVV6rulpFw3vQkhhBBSTp2vDFB1bsOqbdUuHZf6IatV06RxdenSBZ07d8a5c+e4p2eysrIgEAiwbds2uLu7IzU1Fe7u7tDW1saVK1caJI4TJ07g66+/RlpaGvLz87nld+7cgZeXF0pKSjB+/Hh88cUXAICwsDCsWLECALBw4UK4urpKrC89PR1mZmZcEfDixYsxZsyYBokdoH6podWpv2J18PDhQ6alpcUA0L8G+qelpcUePnxIx0WO9j+RTfHx8QwAi4+Pr/F7DQ0Nyy1btGgRc3R0ZAcPHuSWpaWlMXt7e6nWmZeXx4qLi2sUx8uXL9n79+/LxSMUCtndu3dZcXExs7a2ZqmpqayoqIj16tWLZWZmsry8PNa7d2/25s0biffVJN4ytd2P1C/Jdn9VpysDmZmZyMvLo+rcBlJWtZuZmVmjbI+OS/2o7f4n8uv69etYuXIlQkNDERISgpCQEAQGBpZrl5qaiqysLJibm9do/cXFxQgPD0dQUBDS0tJw8eLFGs0XoK+vX26ZWCzGmzdv0LVrVwCAo6Mjrl69iqKiIhgYGHD3zC0tLREXF1duGuPbt2/D2toanTt3xvfff482bdrU6GeSFvVLDauu/VW9FBBSda5souNCSM1YWFhgyJAhmDdvHm7fvo2wsLAK261cuRLffPMNDh48KNV6//zzT+zevRsJCQkQiURYunQpunfvDgAoKCjA0KFDy71HKBRi3bp11a47KysLOjo63Pc6OjrIysqqdPmH2rZtiwcPHqBFixbYu3cvfH19ERAQINXPVFvUL8kmepqAEEI+4OXlhfbt22Pz5s0Vjq4ZExMDPT09GBoaSr3O8PBwXLhwAXPnzoWLiwvatWvHvaampobIyMhax6urq4ucnBzu+5ycHOjp6UFPT48bKO3D5R9SV1eHuro6AMDNzQ1btmypdRxEvsnV0wS3bt2Cn59flW0WLVqEly9f1nobjx49goODA6ytreHr61thm7CwMFhYWMDCwgKHDx+u9bbkGR0LoqgWLlyIb7/9Fjt37sSLFy/Kvf7777/jzp07GD58OAIDA+Hn54fr169XuU4fHx/cvn0bnTp1wsKFCzFixAj88ssvKC4uRkFBAUQiUbl/ZYV/1dHQ0ECLFi3w4MEDMMZw4cIFDB48GAKBAOnp6cjOzoZYLEZMTAz69esn8d7Xr19zX0dERKBbt25SbVNeUD9VA7WqNPifuhTkyCpXV1cWFhbGfR0eHi7xujRFOfWltvtXUY4L38dCUfYjKSXN8Tx48CCbM2cOY4yxq1evshEjRrDi4uIKCwgZY2zVqlW1KiDMzs5mu3btYnl5eTX6GSIiIpi9vT3T1NRk9vb27NixY4wxxhISEpiVlRWzsLBgGzdu5NpfunSJDRo0iA0aNIgFBgZyy6dMmcIYYywkJISZmZmxwYMHsyFDhrC0tLRqY1D2fuljfPdTZeq6f2UyGSguLmZTp05lQqGQeXt7s86dOzPGSk+EmTNnMsYYmzZtGps9ezZzdHRkZmZm7O7du4wxxmxsbNijR49qve3u3btzXx89epR9/fXXEq//9ddfbMyYMdz38+fP534R6pssnHTKfCwUtfNSVnU5nkKhkNnY2LC3b99W2ubevXvMysqKubm51SVMmScL/dLHlLmfKlPX/SuTNQOnT5+Guro6oqKicP/+fezevbvCdj169MCuXbuwf/9+7NmzBxs2bKiwXUZGBjw8PMotnzBhAnx8fCSWlZSUcF9XVHAjTVGOIqFjQQgQFRVVbRuBQIDo6OhGiIZ8jPqpupPJZODu3bsYNGgQAMDQ0LDCx2kAoH///gAAAwMD/Pbbb5Wur1OnTlIX6Hw4E2FFBTfSFOUoEjoWhBBZR/1U3clkAWHXrl1x8+ZNAEBaWlqlI1Z9OHMgq2LyxYyMjAoLdPz9/cu1NTMzw9WrVwEAZ86cgUgkknhdmqIcRULHgpCKyUJxWk5ODuzt7SEUCjFo0CD8+uuvAICbN29y59agQYO4Pz7Z2dkYOnQobGxsYGlpiT/++KPWsckS6qfqTiavDDg5OSE4OBjW1tYwNTVF27Zt67S+mmR5GzZswIwZM1BQUIB+/frB1tYWQOlJvXz5crRp0wabNm3CyJEjAQC+vr5o2bJlneKTZXQsCKlYnz590KdPnyrb/PDDD3XaxldffYVly5bBzs4OU6ZMQUREBHceAKWPBgYEBKBDhw7IzMyElZUVRowYgQEDBnDn2aFDh7jbF0FBQbCyssKqVasQHR2NdevW4eTJk3WKURZQP1UP+CxYqEpBQQFjrLQop2/fvvW+fnkgK4U6ynosqIBQsdT2eMpycdqHcnNzJdqXGTZsGIuJiWGMMXblyhW2ePFixhhjp0+fZvPmzatxTLLSL31MWfupMgpZQAgALi4uyMrKQm5uLjZv3sx3OEqNjgVRZrJcnPahRYsWYfHixRLLnj17hvT0dFhYWAAA+vbti6+//homJibIycnBtWvXKl2fvKF+qm5kNhkIDg7mOwTyP3QsiDKT5eK0Ml9//TV0dXUxZ84cieVBQUESMxVu3LgR48aNg6+vL2JjY+Ht7c3VGcg76qfqRiYLCOuDQCBo1O3t2bMHqqqq5ZavXLmy0WORZY21L6Kjo2FqagoNDQ08fvy4UbZJFJMsF6cBgJ+fH/755x9899135V4LDAyEu7u7xLKyiYjatGkjMYyxsmusvmnv3r0QCoUYPHgwRo8ejTdv3gAA3r9/j3nz5sHBwQEikQh///13o8RTRmavDMiTvLw8BAcHl5sp6smTJ7h37x5PUSk3U1NTXL9+HY6OjnyHQuScLBen5ebm4quvvoKVlRWXKFy6dAlqampITEyElpaWxBwKCxYswNSpU7Fv3z68f/++wgSCNCx3d3fMmDEDQOmHxYMHD8Lb2xtr167FmDFjuELDxtboyUB6ejrc3NygpqYGxhhCQkKQmJiIVatWoaioCDo6Ojh69Cg0NTUhEolgZmaGP//8E2KxGHPmzEFAQACeP3+OY8eOoVu3bhCJRDA2Nsbdu3dRUlKCw4cP45NPPuG2V1hYCC8vL9y/fx8FBQXw8/ODhYUF1q1bhzNnzkBbWxuOjo7l7rXVxKZNm7BgwYJy9/tWr16NFStWwMnJqdbrbiyKdlxatWpVX7uGKDkVFRXs27cPqqqqSE1NxY0bNwCA+0QPAPv37+faf7i8LhMQAUDnzp0rnDmx7CmFNm3aSNQVfMjU1LTc7Yp27dpVOhOjrFK0vklNTY37Ojc3l5tS+tKlSygqKsLGjRvRt29fbNy4Ec2aNd6f6Ea/TRAeHo4hQ4YgIiICkZGRaN26NczNzREREYGoqCgYGRnh2LFjXHsbGxuEhoZCIBAgLi4OoaGh8PX1xd69e7k2AwcOxOXLl+Hm5lbuud89e/bA0NAQ4eHhCAkJ4Q5gUFAQIiIiEB4ejkWLFpWLc9y4ceUu482aNatcu2fPniE+Ph7Dhw+XWH7nzh0AgLGxca33VWNStONCSH1ycXGBjY0NXFxcqDitkSli37Rjxw6YmJggOjqa+xuRlJQES0tLREZGorCwEAcOHKjrrquRRr8yMGnSJKxfvx5ubm7o3LkzVq9ejeTkZKxYsQJisRjPnz+XeAbT3NwcANChQwfucleHDh24+2gAYGlpyf0fEhIisb3ExETExMTg4sWLAMDdI/P394e3tzeKioowb948CIVCifdJW4xS9un/YytXrqzwHqCsUrTjQkh9ot87/ihi3zR//nzMnz8fGzZsgJ+fHzZu3AhdXV2MGDECADBq1ChcuHBB6vXVh0ZPBpo2bYr169cDAGbMmIHQ0FDs3r0ba9asgYWFBZYsWSJRfPNhUU5lBTqxsbEQCASIjY1F9+7dJbZnbGwMgUCAzz//HABQUFAAALCwsIC9vT0yMjLg7OyM+Ph4ifeNGzcOr169klgmEAjKPVaUmpqKr7/+GgDw9OlTTJgwASdOnMD9+/e5rPDp06fw9vbG9u3ba7CnGpeiHRdC5IlAIEBqamqjbOv777/H+fPnUVxcjHnz5sHFxaVRtltbitY35efnQ0NDA0Dp46L5+fkAAHt7e/z+++8QCoW4ceNGo08n3ejJwLlz57Bt2zaoqKhAXV0dQqEQubm5mDlzJnr06IGWLVvWeHSmhIQEBAQEoLi4uNxc0bNnz4aPjw9XeGNmZoYtW7bA2dkZ+fn5yM/Ph7e3d7l1SpvlXblyhftaIBDgxIkTAEqzyw+Xy3IiACjecUlJScGCBQtw+/ZtuLq6wsXFpVxNByHKJjQ0FM+ePZPot2SdovVNa9euRUxMDABAV1eXu32xYcMGzJo1C/n5+fjkk08a/TaBzI5AKK26jvAly2R1pC9pKMJxkYX9SOpPbY5nWloas7S0ZCKRiNnY2LBXr16xq1evMpFIxIRCIRs9ejTLy8tjjJX+zi9atIgNHTqU2djYsKCgIDZ06FDWu3dv9vfff3NtvLy8mIODA7Ozs2PPnz9njDFmaGjIGCsdRW/WrFnM1taWWVlZcSMHfvPNN6x///7M1taWbd68udb7YPr06eyLL75g9vb2bMKECezp06c1Xoc890uMKUbfVJG67l+FHWeAEELqStGK1548eYLCwkJcuXIFLi4uWLJkST3tKSLv5H6cgbo+ukMaBh0XoggUrXjtwyI1R0dHrF27VtpdoTCob6qY3CcDhBDSUBSteK2sSG348OG8FKkR2cXbbYL09HQ4ODg02vZEIhEsLCy4it2wsDBYWFjAwsKiXAFJZcLDw9GkSZNyw9tWNhTxx+7cuQOhUAhLS0uJZ5UdHR2hra1dg5+m4fB9XKZNm4Z27dpJNXaAWCyGh4cHrK2tMXbsWLx+/brK9vKw/4lsOXfuHKytrSESifDkyRMIhUJMnjwZM2fOxLhx4/DixYsarzMhIQFDhgzBgQMH8OWXX0q8Nnv2bPz111+wtbWFra0t/vOf/wAAnJ2dYWNjg4kTJ1ZavBYZGSnxr6InbDw8PPDgwQPY2tpi1apVlU6mJIv47pssLS1hY2OD/v37S/zNCA8Ph4ODA2xtbbF06dIq15mTkwMnJydYW1vD09OTS/YqU9HfqdzcXIhEovofPpmvgoW0tDRmb29fl83XyIdFI0VFRaxXr14sMzOT5eXlsd69e7M3b95U+f7i4mI2YsQI1q9fP4nik3fv3rGRI0eyf//739XGIBQK2d27d1lxcTGztrZmqamp3GtlBUQf4qNQh8/jwhhjjx8/lpgatio7d+7kpnTduXMnW7lyZZXtG2v/E9kkC8dTEYrX+Cog5LtvEovFjDHGXr9+zQwMDBhjjGVmZrKRI0ey/Px8qda5dOlStmfPHu7rvXv3Vtq2ur9TH/dZMlVA6Ovri5MnTwIAioqK0KtXLxQWFmLZsmWws7ND3759sWPHjnLv8/T0RHR0NIDS+zllnwqTkpLg4OAAOzs7TJw4EXl5efUSZ2pqKgwMDKCnpwdNTU1YWloiLi6uyvcEBgZizJgxaN68ucTysqGIP7wkWBGxWIw3b96ga9euaNq0KRwdHSXuIzYkeTkuANC+fXup20ZERMDZ2RkAMHbsWERERFTals/9TwipmDz1TWXDCL99+5YbNfD8+fNo06YNxo4dCwcHB8TGxla5jpr0WbX5O1UX9Voz4OnpiWXLlmH8+PEIDQ2FnZ0dVFVVsXz5cjRv3hxisRimpqZSDx/r5eWFwMBAdOrUCdu3b8euXbskKmkLCgowdOjQcu8TCoVYt25dpevNysqCjo4O931184S/f/8eBw4cwMWLF3HkyBFuedlQxCtXrqz2Z6npNuuTvByXmvpwn1a3P/nc/4SUoeI1SfLUN71//x7Dhg1DcnIyd3vlyZMnSElJwdWrV/H8+XOMGDECf/75Z6XrePXqFVq3bg1A9vqsek0GTExM8PLlS7x48QIBAQHc/ZMdO3bg1KlTUFFRwYsXL8rdZ6us0CY5ORkeHh4ASj/ZfTx9p5qaWq1OLj09PWRnZ3PfVzVPOFA6Kci8efPKTRpR2VDEFdHV1ZWYLrS6bdYneTkuNVV2HA0MDKrdn3zuf0JIxeSpb9LU1MS1a9eQmZmJ/v37Y9KkSdDV1YWtrS00NDTQuXNntGzZEq9evYKurm6F6yjrh3R0dKrtg2r6d6qu6v1pAjc3N2zfvh3p6ekwMzNDdnY29u3bhzt37qCwsBDdu3cvN9e3rq4uMjIyAEDiMoiJiQkOHz7MTRn6cbFFbbM8gUCA9PR0ZGdnQ0tLCzExMfj2228BlM41/vFUxMnJybh69Sp2796NO3fuYOrUqTh37lylQxG/ffuWm02rjIaGBlq0aIEHDx6gS5cuuHDhAn755Zdq92d9kYfjUpWKjotIJMKZM2dgZmYmMde7LO5/Iv/S09Mxa9asRhu9TyQSQSwW4+DBgxAIBJg2bRouX76MkSNHVjv89v79+7FmzRp07twZAHDgwIFy58+HwsLCuA82CxcuhKurK3Jzc+Ho6IjHjx836FDJ8tA3FRQUoFmzZmjatCmaN28ODQ0NaGhowNbWFidPngRjDK9fv0Z2djZ0dHRQVFSEFy9eoF27dhLrKeuzpk2bJtFnvXr1CmpqahKFzFX9nWoI9Z4MTJkyBR07duSeX23dujV69uwJoVCInj17VpjZzJo1C1OmTMGhQ4fQpUsXbvn27dvh6emJwsJCAMCSJUskZgesbZanoqKCTZs2cfNG+/r6cs8KDxs2DCkpKRLtAwMDua9FIhEOHjyI5s2bVzoU8ZEjR/D27dtyU1xu3boVHh4eKCkpgbOzc/1Xg1ZBHo4LACxduhSXLl3Cs2fP4ODggODgYGhqasLR0ZGbCbKMp6cnZs2ahcGDB0NHRwcBAQEAZHP/E1Ibx48fR4cOHQAA69evx/Tp0yX6o6rMnDlTqiuXxcXFWLx4McLDw6GlpQULCws4OjqiRYsWiIyMbPDzRB76poyMDMyYMQMqKioQi8VYtWoV1NXV0a1bN4wYMQI2NjYQi8XYsmULmjRpgrS0NHzxxRc4c+aMxHqWLFmCadOmYc+ePTAwMOA+TG7evBnm5uYYN24c17aqv1MNolZlh/VUvdiYXFxcmFAoZPfu3au0zePHj9nixYvrvK2FCxdyw4xKY9SoUax79+7llsv7sJ/SkOa4REdHs61bt0q9Tr73P5FNFR3PL774gp04cYIxxlhhYSEzNTVlBQUFbOnSpczW1paZmZmxn376iTEmWc0+bdo0FhUVxRhjEk+/JCYmMnt7e2Zra8smTJjA3r17V+t4K3ryQNonbfbt28e6du3KrKys2LJly1hxcXGlbf/66y82ZswY7vv58+ezsLAw7vv6qlqXt/NJmr6pMoGBgezMmTNSt/fw8JD6iYS3b98yGxsbZmNjI7G8rvtXaQYd+rDwrzLt27evl7nKf/zxxxq1P3fuXJ23Ka+kOS5WVlawsrKSep20/4m05KmArSacnJwwdepUAKWfogMCAjB9+vQK21JxbcWk6Zsq4+bmVqP2ZVc1paGtrd0gNVlKkwwQQsjH5KmArSY+/OPu6uqK06dPV5oMNHahGpFNlAwQQpSaPBSwVaWi4tqcnBzuEbbw8HBu2GNZKFQjsqlekoGPC+5I/ajrfqXjUje0/5SDPBSwATUrrvXz88OVK1fQrFkzdO3alfvZZKFQjc6rhlHn/VqrSoP/efjwIdPS0mIA6F8D/dPS0mIPHz6k4yJH+5/IJkUsYKtpcS2fhWrUL8l2f9WEsY+uf9VQRkYGMjMz67IKUgV9ff0qnw+uDB2X+lHb/U9kT0JCAszNzREfH4++ffvyHY7cqst+pH6pYdWlv6rzbYJOnTpRZymD6LgQQmQN9Uuyi7cpjAkhhBAiG+hpAkKIUqECtrqh/aeYKBkghCgFfX19aGlpwd3dne9Q5J6Wlhb09fX5DoPUozoXEBJCiLxoqAI2xhiWLFmChIQEHD9+vNJZ6xrD2rVrcfnyZRw7dowb76C+UWGt4qFkgBBC6ujo0aOYPHkyjh07hokTJ/Iay+vXr2Fqaopu3brh8uXLEqMlElIZSgYIIaQOnj17BmNjY9jb2+PYsWN8hwMAuHz5MoYOHYodO3Zg3rx5fIdD5AAlA4QQUkuMMTg7O+P69etITk6Wqfvo8+bNQ2BgIBITEyVGSSSkIpQMEEJILQUFBcHd3R0nT56UGOJXFrx9+xampqbo0qULwsLC0LQpPUlOKkfJACGE1MLTp09hbGyM4cOH49ChQ3yHU6GwsDA4ODjA398f3t7efIdDZBglA4QQUkOMMTg5OeHmzZtITk6W6Sl/vb29sX//fty5cweGhoZ8h0NkFCUDhBBSQwcOHMC0adNw6tQpODk58R1OlXJzc9GrVy906NABkZGRdLuAVIh+KwghpAaePHmChQsXwt3dXeYTAQDQ1tbG3r17ERUVhW3btvEdDpFRdGWAEEKkxBiDo6Mj/vjjDyQlJfE6uFBNLVy4ELt378bt27fRtWtXvsMhMoaSAUIIkdK+ffswY8YMnD17Fo6OjnyHUyPv3r1D79698emnn+Lq1atQUVHhOyQiQ+g2ASGESOHRo0dYtGgRPD095S4RAIDmzZtj3759iImJwY8//sh3OETG0JUBQgipBmMMI0aMQFJSEpKSktC6dWu+Q6q1zz//HD///DP++OMP9OjRg+9wiIygZIAQQqqxe/duzJ49GxcuXMCIESP4DqdO8vLy0KdPH+jq6uK3336j2wUEAN0mIISQKj18+BCLFy/GzJkz5T4RAEqnH96/fz9u3ryJzZs38x0OkRF0ZYAQQirBGMOQIUNw9+5dJCYmolWrVnyHVG++/PJLbNu2DQkJCejZsyff4RCeUTJACCGV+PnnnzF//nxcunQJQ4YM4TucevX+/Xv07dsXLVq0QExMDJo1a8Z3SIRHdJuAEEIqkJaWBl9fX8yZM0fhEgEA0NTUxP79+xEfHw8/Pz++wyE8oysDhBDykZKSEjg4OODBgwdITExEixYt+A6pwfznP//Bli1bEB8fD1NTU77DITyhZIAQQj6yfft2+Pj44MqVK7C3t+c7nAaVn58Pc3NzaGhoIDY2FqqqqnyHRHhAtwkIIeQD9+/fx5IlS+Dl5aXwiQAAaGhoYP/+/bh9+za+++47vsMhPKErA4QQpVdQUIA//vgD/fv3h62tLR49eoQ7d+5AW1ub79AazfLly+Hn54e4uDh88sknePfuHQQCAd9hkUZCyQAhROnt2bMHn332Gf773/9i0aJFiIiIgEgk4jusRiUWi9GvXz80a9YMgwcPxo0bNxAbG8t3WKSR0G0CQojSu3fvHnR0dLB06VLMnTsXOjo6fIfU6LKzs/HNN98gMTERSUlJuHfvHt8hkUZEyQAhROk9ePAAb968gba2Nk6dOoWRI0eipKSE77Aa1Y4dOzBu3Dj06tULkZGRePXqFd68ecN3WKSRUDJACFF6sbGxePPmDV6+fAk7Oztcv34dTZsqV/e4YsUK/Pjjj3j48CGXCP399988R0Uai3L9thNCSAXevn2Ldu3aISYmBocOHUKnTp34DqnRqaqqYsGCBbh37x7c3NwAAE+fPuU5KtJYqICQEKL0iouLafa+j9A+US6UDBBCCCFKjmamIIRIJSMjA5mZmXyHIff09fXrfBuCjkX9qI9joSgoGSCEVCsjIwNGRkbIy8vjOxS5p6WlhZSUlFr/EaJjUX/qeiwUCSUDhJBqZWZmIi8vD4GBgTAyMuI7HLmVkpICd3d3ZGZm1voPEB2L+lEfx0KRUDJACJGakZER+vbty3cYBHQsSP2iRwsJIYQQJUfJACGEEKLkKBkghMiEW7duwc/Pr8o2ixYtwsuXL2u9jUePHsHBwQHW1tbw9fWtsE1YWBgsLCxgYWGBw4cP13pb8oyOhRJihBBSjfj4eAaAxcfH8x1Knbi6urKwsDDu6/DwcInXi4qKWK9evVhmZibLy8tjvXv3Zm/evKm37dfHfqRjUT8UZT/WF7oyQAhpVCUlJfDw8IC1tTV8fHxgYGAAAIiMjMSsWbMAAJ6enpgzZw5Gjx6Nvn37cjPoiUQiPH78uNbbTkhIgJ2dHQBg7NixiIiIkHg9NTUVBgYG0NPTg6amJiwtLREXF1fr7ck6OhakDD1NQAhpVKdPn4a6ujqioqJw//597N69u8J2PXr0wK5du7B//37s2bMHGzZsqLBdRkYGPDw8yi2fMGECfHx8JJZ9OBOhjo4OsrKyJF7PysqSmL64ojaKhI4FKUPJACGkUd29exeDBg0CABgaGkJfX7/Cdv379wcAGBgY4Lfffqt0fZ06dUJkZKRU2/5wJsKcnBzo6elJvK6np4fs7Owq2ygSOhakDN0mIIQ0qq5du+LmzZsAgLS0tEqH1W3SpAn3NatiCpWMjAyIRKJy//z9/cu1NTMzw9WrVwEAZ86cgUgkknhdIBAgPT0d2dnZEIvFiImJQb9+/Wr6I8oNOhakDF0ZIIQ0KicnJwQHB8Pa2hqmpqZo27ZtndZXk0+jGzZswIwZM1BQUIB+/frB1tYWQGll/PLly9GmTRts2rQJI0eOBAD4+vqiZcuWdYpPltGxIGVo1kJCSLUSEhJgbm6O+Pj4ehn1rrCwEKqqqkhNTYWLiwvi4+PrIUrZVx/7kY5F/ajv/Sjv6MoAIaTRubi4ICsrC7m5udi8eTPf4Sg1OhYEoGSAEMKD4OBgvkMg/0PHggBUQEgIkXMCgaBRtrN//3506dKFK4rLyMholO3Kk8Y6FtHR0TA1NYWGhkadxjog/4+uDBBCiJRmzpyJFStW8B2G0jM1NcX169fh6OjIdygKg5IBQkiDSE9Ph5ubG9TU1MAYQ0hICBITE7Fq1SoUFRVBR0cHR48ehaamJkQiEczMzPDnn39CLBZjzpw5CAgIwPPnz3Hs2DF069YNIpEIxsbGuHv3LkpKSnD48GF88skn3PYKCwvh5eWF+/fvo6CgAH5+frCwsMC6detw5swZaGtrw9HREYsXL671z3TgwAFcvHgRNjY2+OabbySelZdlinYsWrVqVV+7hvyPfPwmE0LkTnh4OIYMGYKIiAhERkaidevWMDc3R0REBKKiomBkZIRjx45x7W1sbBAaGgqBQIC4uDiEhobC19cXe/fu5doMHDgQly9fhpubW7mJdPbs2QNDQ0OEh4cjJCSE+0MTFBSEiIgIhIeHY9GiReXiHDduXLnn4suG4v2Qk5MTUlJScPXqVfzzzz8ICAiopz3V8BTtWJD6R1cGCCENYtKkSVi/fj3c3NzQuXNnrF69GsnJyVixYgXEYjGeP38u8dy4ubk5AKBDhw4wNDTkvi4bmAYALC0tuf9DQkIktpeYmIiYmBhcvHgRQOmIdQDg7+8Pb29vFBUVYd68eRAKhRLvk7aA7sOhcV1dXXH69GlMnz5dqvfyTdGOBal/lAwQQhpE06ZNsX79egDAjBkzEBoait27d2PNmjWwsLDAkiVLJEaz+3CUu8pGvIuNjYVAIEBsbCy6d+8usT1jY2MIBAJ8/vnnAICCggIAgIWFBezt7ZGRkQFnZ+dyz9GPGzcOr169klgmEAjKjdOfk5OD1q1bAyj9pP3x9mWZoh0LUv8oGSCENIhz585h27ZtUFFRgbq6OoRCIXJzczFz5kz06NEDLVu2rPGIcgkJCQgICEBxcXG5+e1nz54NHx8fbiQ7MzMzbNmyBc7OzsjPz0d+fj68vb3LrVPaT6N+fn64cuUKmjVrhq5du2Lt2rU1ip1PinYsUlJSsGDBAty+fRuurq5wcXEpNxESqRkagZAQUi1ZGK1NJBIhMDAQHTp04GX79UEWRyCsDToWiocKCAkhhBAlR7cJCCFyQdoJcEjDo2OheOjKACGEEKLkKBkghDSo9PR0ODg4NNr2RCIRLCwskJqaCgAICwuDhYUFLCwsyhW6fSwyMhJt27blnnG/efNmle3v3LkDoVAIS0tLiUl+HB0doa2tXfcfpp7xfSymTZuGdu3aSTV2gFgshoeHB6ytrTF27Fi8fv26yvbydixkDSUDhBCFc/z4cQgEAhQXF2Px4sU4d+4cwsPD8d133+Ht27dVvnfUqFGIjIxEZGQkBgwYUGVbb29v7Nu3D9HR0Th9+jTu378PoLR6/9NPP623n0eelR0LAFi/fj0OHTok1fsCAgJgYGCAqKgojBw5Elu2bKmyPR2LuqFkgBBSY76+vjh58iQAoKioCL169UJhYSGWLVsGOzs79O3bFzt27Cj3Pk9PT0RHRwMo/RRe9gkxKSkJDg4OsLOzw8SJE5GXl1cvcaampsLAwAB6enrQ1NSEpaUl4uLiqnzPpUuXIBQK4eXlVWUcYrEYb968QdeuXdG0aVM4OjpKDMrTWOTlWABA+/btpW4bEREBZ2dnAMDYsWMRERFRaVtZORbyjJIBQkiNeXp6csPxhoaGws7ODqqqqli+fDnCw8Nx/fp1fP/99ygsLJRqfV5eXti7dy/Cw8MhEomwa9cuidcLCgrKDVMrEomqnTQoKytLYuRAHR0dZGVlVdre3Nwcd+/eRXR0NPT19fHdd9/V27obirwci5r6cP9Wt29l5VjIM3qagBBSYyYmJnj58iVevHiBgIAALF26FACwY8cOnDp1CioqKnjx4gVevHgh8b7KRrNLTk6Gh4cHgNJPeSKRSOJ9ampqtapg19PTQ3Z2Nvd9Tk4O9PT0Km3fokUL7mt3d3duBL2K6OrqcsPsSrPuhiIvx6Kmyo6dgYFBtftWVo6FPKNkgBBSK25ubti+fTvS09NhZmaG7Oxs7Nu3D3fu3EFhYSG6d++Oj8c009XVRUZGBgBIXK43MTHB4cOH0bZtWwD/P3xtmYKCAgwdOrRcDEKhEOvWras0RoFAgPT0dGRnZ0NLSwsxMTH49ttvAQAZGRno1KmTRPvXr19zM+J9OOTw27dvudn9ymhoaKBFixZ48OABunTpggsXLuCXX36peqc1EHk4FlWp6FiIRCKcOXMGZmZmOHPmDJeUyPqxkFeUDBBCamXKlCno2LEjNyxv69at0bNnTwiFQvTs2bPCT2azZs3ClClTcOjQIXTp0oVbvn37dnh6enKXspcsWYLhw4dzr9f206iKigo2bdqEkSNHAii9v1427O6wYcOQkpIi0T4oKAj79u2DpqYmdHV1uVn6jhw5grdv35abcnfr1q3w8PBASUkJnJ2duUK5xiYPxwIAli5dikuXLuHZs2dwcHBAcHAwNDU14ejoiDt37ki09fT0xKxZszB48GDo6Ohwt0Jk/VjILUYIIdWIj49nAFh8fDzfoVTLxcWFCYVCdu/evUrbPH78mC1evFjqdS5cuJA9f/5c6vajRo1i3bt3L7e8Pvajoh2L6OhotnXrVqnXKUvHQpHQ3ASEkGrROO71Q1HmJlAEtB8l0dMEhBBCiJKjZIAQQghRclRASAiR2scFd6Rm6nP/0bGoG9p/kigZIIRUS19fH1paWnB3d+c7FLmnpaUFfX39Wr+fjkX9qeuxUCRUQEgIkUpGRgYyMzP5DkPu6evrl3umvqboWNSP+jgWioKSAUIIIUTJUQEhIYQQouQoGSCEEEKUHCUDhBBCiJKjZIAQQghRcpQMEEIIIUqOkgFCCCFEyVEyQAghhCg5SgYIIYQQJUfJACGEEKLkKBkghBBClBwlA4QQQoiSo2SAEEIIUXKUDBBCCCFKjpIBQgghRMlRMkAIIYQoOUoGCCGEECVHyQAhhBCi5CgZIIQQQpQcJQOEEEKIkqNkgBBCCFFylAwQQgghSo6SAUIIIUTJUTJACCGEKDlKBgghhBAlR8kAIYQQouT+D+3FuILBMBFqAAAAAElFTkSuQmCC\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree.plot_tree(clf);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-30T02:35:55.035966100Z",
     "start_time": "2024-05-30T02:35:54.927348200Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "0.9444444444444444"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = clf.score(Xtest, Ytest) #返回预测的准确度  # score  Xtest -> yhat -> Ytest\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-30T02:35:57.836382700Z",
     "start_time": "2024-05-30T02:35:57.787628400Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "(54, 13)"
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xtest.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-30T02:41:53.744012400Z",
     "start_time": "2024-05-30T02:41:53.716227Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "array([ 1.32645365e+01,  1.40814364e+00,  2.62974818e+00,  1.49877860e+01,\n        1.16773561e+02,  2.08337153e+00,  2.74399989e+00, -9.48977292e-01,\n        1.30740895e+00,  5.96062070e+00,  9.94592081e-01,  2.87318934e+00,\n        1.05915921e+03])"
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "wd_data = Xtest[0,:]+np.random.randn(13)\n",
    "wd_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-30T02:46:24.205502800Z",
     "start_time": "2024-05-30T02:46:24.158321800Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "array([0])"
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# clf.predict(wd_data)\n",
    "# wd_data = wd_data.reshape(1,-1)\n",
    "# wd_data\n",
    "clf.predict(wd_data.reshape(1,-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-30T02:49:21.775435Z",
     "start_time": "2024-05-30T02:49:21.757890100Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "array([0, 2, 1, 0, 1, 0, 0, 2, 1, 1, 2, 2, 0, 1, 2, 1, 0, 0, 2, 0, 1, 0,\n       0, 1, 1, 1, 1, 1, 1, 2, 0, 0, 1, 0, 0, 0, 2, 1, 1, 2, 1, 0, 1, 1,\n       1, 0, 2, 1, 2, 0, 2, 2, 0, 2])"
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat = clf.predict(Xtest)# 预测方法\n",
    "yhat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-30T02:49:23.971824800Z",
     "start_time": "2024-05-30T02:49:23.955410500Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "array([0, 2, 1, 0, 1, 1, 0, 2, 1, 1, 2, 2, 0, 1, 2, 1, 0, 0, 1, 0, 1, 0,\n       0, 1, 1, 1, 1, 1, 1, 2, 0, 0, 1, 0, 0, 0, 2, 1, 1, 2, 0, 0, 1, 1,\n       1, 0, 2, 1, 2, 0, 2, 2, 0, 2])"
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ytest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-30T02:49:26.395199800Z",
     "start_time": "2024-05-30T02:49:26.378886500Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "3"
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(~(yhat == Ytest)).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-30T02:49:34.301284500Z",
     "start_time": "2024-05-30T02:49:33.672028400Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1800x1000 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制决策树的树状图\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(18,10))     \n",
    "tree.plot_tree(clf  \n",
    "               ,feature_names=wine.feature_names\n",
    "               ,class_names=wine.target_names\n",
    "               ,node_ids=True  \n",
    "               ,filled=True    \n",
    "               ,rounded=True   \n",
    "               ,fontsize=11 \n",
    "              );"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-30T02:49:38.010125400Z",
     "start_time": "2024-05-30T02:49:37.971832300Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "array([0.        , 0.        , 0.02389053, 0.        , 0.02036125,\n       0.        , 0.4175378 , 0.        , 0.        , 0.40621672,\n       0.        , 0.        , 0.1319937 ])"
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#特征重要性\n",
    "clf.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-30T02:51:20.251449Z",
     "start_time": "2024-05-30T02:51:20.223278400Z"
    }
   },
   "outputs": [],
   "source": [
    "feature_name = ['酒精','苹果酸','灰','灰的碱性','镁'\n",
    "                ,'总酚','类黄酮','非黄烷类酚类'\n",
    "                ,'花青素','颜色强度','色调'\n",
    "                ,'od280/od315稀释葡萄酒','脯氨酸']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-30T02:51:21.861439900Z",
     "start_time": "2024-05-30T02:51:21.811747900Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "[(1, 'a'), (2, 'b')]"
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip([1,2],['a','b']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-30T02:51:23.378354800Z",
     "start_time": "2024-05-30T02:51:23.336162300Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "[('酒精', 0.0),\n ('苹果酸', 0.0),\n ('灰', 0.0238905309250137),\n ('灰的碱性', 0.0),\n ('镁', 0.020361247947454843),\n ('总酚', 0.0),\n ('类黄酮', 0.417537798818587),\n ('非黄烷类酚类', 0.0),\n ('花青素', 0.0),\n ('颜色强度', 0.40621672426724315),\n ('色调', 0.0),\n ('od280/od315稀释葡萄酒', 0.0),\n ('脯氨酸', 0.13199369804170136)]"
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看每个特征的重要性\n",
    "[*zip(feature_name,clf.feature_importances_)] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-29T07:49:57.194169Z",
     "start_time": "2024-02-29T07:49:57.174533Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('脯氨酸', 0.40372252507520007),\n",
       " ('od280/od315稀释葡萄酒', 0.31038694721374854),\n",
       " ('总酚', 0.08813265820565089),\n",
       " ('色调', 0.08067801592919667),\n",
       " ('酒精', 0.04608942054197528),\n",
       " ('非黄烷类酚类', 0.024462418623002562),\n",
       " ('类黄酮', 0.023647004668902464),\n",
       " ('花青素', 0.022881009742323575),\n",
       " ('苹果酸', 0.0),\n",
       " ('灰', 0.0),\n",
       " ('灰的碱性', 0.0),\n",
       " ('镁', 0.0),\n",
       " ('颜色强度', 0.0)]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#按照特征重要性降序排列\n",
    "sorted([*zip(feature_name,clf.feature_importances_)],key= lambda x:x[1],reverse=True) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "多次运行下面这段代码，看看会发生什么："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-30T02:53:51.718808700Z",
     "start_time": "2024-05-30T02:53:51.288884800Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8703703703703703\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1800x1000 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 多次运行此段代码\n",
    "clf = DTC()\n",
    "clf = clf.fit(Xtrain, Ytrain)\n",
    "score = clf.score(Xtest, Ytest)\n",
    "print(score)\n",
    "\n",
    "plt.figure(figsize=(18,10))     \n",
    "tree.plot_tree(clf  \n",
    "               ,feature_names=wine.feature_names\n",
    "               ,class_names=wine.target_names\n",
    "               ,node_ids=True  \n",
    "               ,filled=True    \n",
    "               ,rounded=True   \n",
    "               ,fontsize=11 \n",
    "              );"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "多次运行之后，你会发现score会在某个值附近波动，并且画出来的每一棵树都不一样。思考一下：它为什么会不稳定呢？如果使用其他数据集，它还会不稳定吗？\n",
    "\n",
    "我们之前提到过，无论决策树模型如何进化，在分枝上的本质都还是追求某个纯度相关的指标的优化，而正如我们提到的，纯度是基于节点来计算的，也就是说，决策树在建树时，是靠优化节点来追求一棵优化的树，但最优的节点能够保证最优的树吗？\n",
    "\n",
    "集成算法被用来解决这个问题：sklearn表示，既然一棵树不能保证最优，那就建更多的不同的树，然后从中取最好的。怎样从一组数据集中建不同的树？在每次分枝时，不从使用全部特征，而是随机选取一部分特征，从中选取不纯度相关指标最优的作为分枝用的节点。这样，每次生成的树也就不同了。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-29T08:00:12.254008Z",
     "start_time": "2024-02-29T08:00:12.242864Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9444444444444444\n"
     ]
    }
   ],
   "source": [
    "# 再多次运行此段代码，看score是否还会变化\n",
    "clf = DTC(random_state=0)\n",
    "clf = clf.fit(Xtrain, Ytrain)\n",
    "score = clf.score(Xtest, Ytest) \n",
    "print(score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **random_state & splitter**\n",
    "\n",
    "random_state用来设置分枝中的随机模式的参数，默认None，在高维度时随机性会表现更明显，低维度的数据（比如鸢尾花数据集），随机性几乎不会显现。输入任意整数，会一直长出同一棵树，让模型稳定下来。\n",
    "\n",
    "splitter也是用来控制决策树中的随机选项的，有两种输入值：\n",
    "\n",
    "- 输入”best\"，决策树在分枝时虽然随机，但是还是会优先选择更重要的特征进行分枝（重要性可以通过属性feature_importances_查看）\n",
    "- 输入“random\"，决策树在分枝时会更加随机，树会因为含有更多的不必要信息而更深更大，并因这些不必要信息而降低对训练集的拟合。\n",
    "\n",
    "这也是防止过拟合的一种方式。所谓的<font color=\"orange\">**过拟合指的是模型在训练集上表现很好，在测试集上却表现糟糕**。<font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-29T08:00:14.086083Z",
     "start_time": "2024-02-29T08:00:14.069153Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型对训练集的预测准确率为： 1.0\n",
      "模型对测试集的预测准确率为： 0.9629629629629629\n"
     ]
    }
   ],
   "source": [
    "clf = DTC(random_state=6)\n",
    "clf = clf.fit(Xtrain, Ytrain)\n",
    "train_score = clf.score(Xtrain, Ytrain)\n",
    "test_score = clf.score(Xtest, Ytest)\n",
    "print(\"模型对训练集的预测准确率为：\",train_score)\n",
    "print(\"模型对测试集的预测准确率为：\",test_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "当你预测到你的模型会过拟合，用这两个参数来帮助你降低树建成之后过拟合的可能性。\n",
    "\n",
    "当然，树一旦建成，我们依然是使用剪枝参数来防止过拟合。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-29T08:00:21.355140Z",
     "start_time": "2024-02-29T08:00:21.347607Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型对训练集的预测准确率为： 1.0\n",
      "模型对测试集的预测准确率为： 0.9629629629629629\n"
     ]
    }
   ],
   "source": [
    "clf = DTC(random_state=0,splitter=\"random\")\n",
    "clf = clf.fit(Xtrain, Ytrain)\n",
    "train_score = clf.score(Xtrain, Ytrain)\n",
    "test_score = clf.score(Xtest, Ytest)\n",
    "print(\"模型对训练集的预测准确率为：\",train_score)\n",
    "print(\"模型对测试集的预测准确率为：\",test_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2.2 剪枝参数详解\n",
    "\n",
    "**决策树天生就是容易过拟合的一个算法**：在不加限制的情况下，一棵决策树会生长到衡量纯度的指标最优（比如CART树的基尼系数），或者没有更多的特征可用为止。这样的决策树往往对训练数据有过于优秀的解释性，那么它找出的规则必然包含了训练样本中的噪声，而我们收集的样本数据不可能和训练数据的状况完全一致，所以这样的模型很容易对未知数据的拟合程度不足。\n",
    "\n",
    "为了让决策树有更好的泛化性，我们要对决策树进行剪枝。**剪枝策略对决策树的影响巨大，正确的剪枝策略是优化决策树算法的核心。**sklearn为我们提供了不同的剪枝策略："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **max_depth**\n",
    "\n",
    "限制树的最大深度，超过设定深度的树枝全部剪掉\n",
    "\n",
    "这是用得最广泛的剪枝参数，在高维度低样本量时非常有效。决策树多生长一层，对样本量的需求会增加一倍，所以限制树深度能够有效地限制过拟合。在集成算法中也非常实用。实际使用时，建议从=3开始尝试，看看拟合的效果再决定是否增加设定深度。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **min_samples_leaf & min_samples_split** \n",
    "\n",
    "min_samples_leaf限定，一个节点在分枝后的每个子节点都必须包含至少min_samples_leaf个训练样本，否则分枝就不会发生，或者，分枝会朝着满足每个子节点都包含min_samples_leaf个样本的方向去发生。\n",
    "\n",
    "一般搭配max_depth使用，在回归树中有神奇的效果，可以让模型变得更加平滑。这个参数的数量设置得太小会引起过拟合，设置得太大就会阻止模型学习数据。一般来说，建议从=5开始使用。如果叶节点中含有的样本量变化很大，建议输入浮点数作为样本量的百分比来使用。同时，这个参数可以保证每个叶子的最小尺寸，可以在回归问题中避免低方差，过拟合的叶子节点出现。对于类别不多的分类问题，=1通常就是最佳选择。\n",
    "\n",
    "min_samples_split限定，一个节点必须要包含至少min_samples_split个训练样本，这个节点才允许被分枝，否则分枝就不会发生。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-29T08:03:14.933247Z",
     "start_time": "2024-02-29T08:03:14.917709Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型对训练集的预测准确率为： 0.9193548387096774\n",
      "模型对测试集的预测准确率为： 0.8703703703703703\n"
     ]
    }
   ],
   "source": [
    "clf = DTC(random_state=0\n",
    "          ,splitter=\"random\"\n",
    "          ,max_depth=4\n",
    "          ,min_samples_leaf=10\n",
    "          ,min_samples_split=10\n",
    "         )\n",
    "Xtrain, Xtest, Ytrain, Ytest = train_test_split(wine.data,wine.target,test_size=0.3)\n",
    "clf = clf.fit(Xtrain, Ytrain)\n",
    "train_score = clf.score(Xtrain, Ytrain)\n",
    "test_score = clf.score(Xtest, Ytest)\n",
    "print(\"模型对训练集的预测准确率为：\",train_score)\n",
    "print(\"模型对测试集的预测准确率为：\",test_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **max_features & min_impurity_decrease**\n",
    "\n",
    "一般搭配max_depth使用，用作树的”精修“\n",
    "\n",
    "max_features限制分枝时考虑的特征个数，超过限制个数的特征都会被舍弃。和max_depth异曲同工，max_features是用来限制高维度数据的过拟合的剪枝参数，但其方法比较暴力，是直接限制可以使用的特征数量而强行使决策树停下的参数，在不知道决策树中的各个特征的重要性的情况下，强行设定这个参数可能会导致模型学习不足。如果希望通过降维的方式防止过拟合，建议使用PCA，ICA或者特征选择模块中的降维算法。\n",
    "\n",
    "min_impurity_decrease限制信息增益的大小，信息增益小于设定数值的分枝不会发生。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2.3 重要属性和接口\n",
    "\n",
    "属性是在模型训练之后，能够调用查看的模型的各种性质。对决策树来说，最重要的是feature_importances_，能够查看各个特征对模型的重要性。\n",
    "\n",
    "sklearn中许多算法的接口都是相似的，比如说我们之前已经用到的fit和score，几乎对每个算法都可以使用。除了这两个接口之外，决策树最常用的接口还有apply和predict。apply中输入测试集返回每个测试样本所在的叶子节点的索引，predict输入测试集返回每个测试样本的标签。返回的内容一目了然并且非常容易，大家感兴趣可以自己下去试试看。\n",
    "\n",
    "在这里不得不提的是，**所有接口中要求输入特征矩阵X的部分，至少是一个二维矩阵。sklearn不接受任何一维矩阵作为特征矩阵被输入。**如果你的数据的确只有一个特征，那必须用reshape(-1,1)来给矩阵增维；如果你的数据只有一个特征和一个样本，使用reshape(1,-1)来给你的数据增维。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-29T08:04:36.869732Z",
     "start_time": "2024-02-29T08:04:36.514044Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,10))     \n",
    "tree.plot_tree(clf  \n",
    "               ,feature_names=wine.feature_names\n",
    "               ,class_names=wine.target_names\n",
    "               ,node_ids=True  \n",
    "               ,filled=True    \n",
    "               ,rounded=True   \n",
    "               ,fontsize=11 \n",
    "              );\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-29T08:04:55.995745Z",
     "start_time": "2024-02-29T08:04:55.979555Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12, 10, 10, 12,  3, 12,  7,  3, 10, 12,  3, 10,  7,  7, 12,  6,  6,\n",
       "       10,  5, 12, 10, 10, 12, 11,  3, 12,  5,  6, 11,  6,  3, 12,  3, 12,\n",
       "       10,  3, 10, 12, 10, 12, 10, 12, 10, 12, 12, 10, 10, 12, 12,  6, 12,\n",
       "       10, 12, 11])"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#apply返回每个测试样本所在的叶子节点的索引\n",
    "clf.apply(Xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 0, 2, 2, 2, 1, 1, 0, 2, 2, 1, 0, 0, 0, 2, 2, 2, 2, 1, 0, 0,\n",
       "       0, 0, 0, 2, 2, 2, 1, 1, 1, 2, 0, 1, 0, 2, 2, 1, 0, 1, 0, 1, 0, 2,\n",
       "       0, 0, 1, 0, 2, 1, 2, 1, 1, 2])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#predict返回每个测试样本的分类/回归结果\n",
    "clf.predict(Xtest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "至此，我们已经学完了分类树DecisionTreeClassifier和用决策树绘图（plot_tree）的所有基础。我们讲解了决策树的基本流程，分类树的八个参数，一个属性，四个接口，以及绘图所用的代码。\n",
    "\n",
    "八个参数：Criterion，两个随机性相关的参数（random_state，splitter），五个剪枝参数（max_depth, min_samples_split，min_samples_leaf，max_feature，min_impurity_decrease）\n",
    "\n",
    "一个属性：feature_importances_\n",
    "\n",
    "四个接口：fit，score，apply，predict\n",
    "\n",
    "有了这些知识，基本上分类树的使用大家都能够掌握了，接下来再到实例中去磨练就好。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 实例：泰坦尼克号幸存者的预测"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "泰坦尼克号的沉没是世界上最严重的海难事故之一，今天我们通过分类树模型来预测一下哪些人可能成为幸存者。本次使用的数据集来源于Kaggle平台，已经给大家下载好了，接下来我们就来执行我们的代码。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 导入所需要的库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-01T07:50:48.141145Z",
     "start_time": "2024-03-01T07:50:46.992191Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier as DTC\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 导入数据集，并探索数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-01T07:54:25.926401Z",
     "start_time": "2024-03-01T07:54:25.886333Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived  Pclass  \\\n",
       "PassengerId                     \n",
       "1                   0       3   \n",
       "2                   1       1   \n",
       "3                   1       3   \n",
       "4                   1       1   \n",
       "5                   0       3   \n",
       "\n",
       "                                                          Name     Sex   Age  \\\n",
       "PassengerId                                                                    \n",
       "1                                      Braund, Mr. Owen Harris    male  22.0   \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "3                                       Heikkinen, Miss. Laina  female  26.0   \n",
       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
       "5                                     Allen, Mr. William Henry    male  35.0   \n",
       "\n",
       "             SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "PassengerId                                                          \n",
       "1                1      0         A/5 21171   7.2500   NaN        S  \n",
       "2                1      0          PC 17599  71.2833   C85        C  \n",
       "3                0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "4                1      0            113803  53.1000  C123        S  \n",
       "5                0      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"data/Titanic.csv\",index_col = 0)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-01T07:50:52.526523Z",
     "start_time": "2024-03-01T07:50:52.505175Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 11)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-01T07:50:52.699201Z",
     "start_time": "2024-03-01T07:50:52.689787Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 891 entries, 1 to 891\n",
      "Data columns (total 11 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Survived  891 non-null    int64  \n",
      " 1   Pclass    891 non-null    int64  \n",
      " 2   Name      891 non-null    object \n",
      " 3   Sex       891 non-null    object \n",
      " 4   Age       714 non-null    float64\n",
      " 5   SibSp     891 non-null    int64  \n",
      " 6   Parch     891 non-null    int64  \n",
      " 7   Ticket    891 non-null    object \n",
      " 8   Fare      891 non-null    float64\n",
      " 9   Cabin     204 non-null    object \n",
      " 10  Embarked  889 non-null    object \n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 83.5+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 数据预处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这一部分的数据预处理，不涉及到训练集和测试集之间相互影响的那些操作。<br>比如删除信息量少的列、删除与预测没有太大关系的列、文本型变量的编码等。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-01T07:54:29.538116Z",
     "start_time": "2024-03-01T07:54:29.526366Z"
    }
   },
   "outputs": [],
   "source": [
    "#删除缺失值过多的列，和观察判断来说和预测的y没有关系的列\n",
    "data.drop([\"Cabin\",\"Name\",\"Ticket\"],inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-01T07:50:56.065263Z",
     "start_time": "2024-03-01T07:50:56.031515Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
       "PassengerId                                                                \n",
       "1                   0       3    male  22.0      1      0   7.2500        S\n",
       "2                   1       1  female  38.0      1      0  71.2833        C\n",
       "3                   1       3  female  26.0      0      0   7.9250        S\n",
       "4                   1       1  female  35.0      1      0  53.1000        S\n",
       "5                   0       3    male  35.0      0      0   8.0500        S\n",
       "...               ...     ...     ...   ...    ...    ...      ...      ...\n",
       "887                 0       2    male  27.0      0      0  13.0000        S\n",
       "888                 1       1  female  19.0      0      0  30.0000        S\n",
       "889                 0       3  female   NaN      1      2  23.4500        S\n",
       "890                 1       1    male  26.0      0      0  30.0000        C\n",
       "891                 0       3    male  32.0      0      0   7.7500        Q\n",
       "\n",
       "[891 rows x 8 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-01T07:42:07.583252Z",
     "start_time": "2024-03-01T07:42:07.573781Z"
    }
   },
   "outputs": [],
   "source": [
    "# temp.drop(columns=[\"a\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-01T07:42:10.701789Z",
     "start_time": "2024-03-01T07:42:10.692903Z"
    }
   },
   "outputs": [],
   "source": [
    "# temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-01T07:42:14.253064Z",
     "start_time": "2024-03-01T07:42:14.245398Z"
    }
   },
   "outputs": [],
   "source": [
    "# temp.drop(columns=[\"a\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-01T07:42:15.746984Z",
     "start_time": "2024-03-01T07:42:15.742059Z"
    }
   },
   "outputs": [],
   "source": [
    "# temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-01T07:42:17.955957Z",
     "start_time": "2024-03-01T07:42:17.933231Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 891 entries, 1 to 891\n",
      "Data columns (total 8 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Survived  891 non-null    int64  \n",
      " 1   Pclass    891 non-null    int64  \n",
      " 2   Sex       891 non-null    object \n",
      " 3   Age       714 non-null    float64\n",
      " 4   SibSp     891 non-null    int64  \n",
      " 5   Parch     891 non-null    int64  \n",
      " 6   Fare      891 non-null    float64\n",
      " 7   Embarked  889 non-null    object \n",
      "dtypes: float64(2), int64(4), object(2)\n",
      "memory usage: 62.6+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-01T07:46:25.687445Z",
     "start_time": "2024-03-01T07:46:25.664807Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId\n",
       "1       True\n",
       "2      False\n",
       "3      False\n",
       "4      False\n",
       "5       True\n",
       "       ...  \n",
       "887     True\n",
       "888    False\n",
       "889    False\n",
       "890     True\n",
       "891     True\n",
       "Name: Sex, Length: 891, dtype: bool"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data.head()\n",
    "# def transfer_(x):\n",
    "#     if x=='male':\n",
    "#         return 1\n",
    "#     else:\n",
    "#         return 0\n",
    "# data.Sex.apply(transfer_)\n",
    "# mapping = {\n",
    "#     \"male\":1\n",
    "#     ,\"female\":0\n",
    "# }\n",
    "# data.Sex.map(mapping)\n",
    "# data.Sex\n",
    "# (data.Sex == \"male\") + 0\n",
    "data[\"Sex\"]== \"male\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-01T07:54:33.553206Z",
     "start_time": "2024-03-01T07:54:33.536766Z"
    }
   },
   "outputs": [],
   "source": [
    "#将二分类变量转换为数值型变量\n",
    "data[\"Sex\"] = (data[\"Sex\"]== \"male\").astype(\"int\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-01T07:55:49.285428Z",
     "start_time": "2024-03-01T07:55:49.275695Z"
    }
   },
   "outputs": [],
   "source": [
    "# data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-01T07:55:49.912424Z",
     "start_time": "2024-03-01T07:55:49.902136Z"
    }
   },
   "outputs": [],
   "source": [
    "### 删除缺失值\n",
    "# data[\"Embarked\"].isnull() # 可以通过布尔索引来完成Series、DataFrame的过滤\n",
    "# data = pd.DataFrame({\n",
    "#     \"a\":[1,2,3,4,5]\n",
    "#     ,\"aa\":[\"a\",'b','c','d','e']\n",
    "# })\n",
    "# # data\n",
    "# # 去除a这列偶数值\n",
    "# # data[\"a\"][data[\"a\"] % 2  == 0]\n",
    "# # data[\"a\"]\n",
    "# data[data[\"a\"] % 2  == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-01T07:55:21.212935Z",
     "start_time": "2024-03-01T07:55:21.201444Z"
    }
   },
   "outputs": [],
   "source": [
    "# data = data[~data[\"Embarked\"].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-01T07:55:30.544093Z",
     "start_time": "2024-03-01T07:55:30.534094Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data[\"Embarked\"].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "astype能够将一个pandas对象转换为某种类型,这里使用的astype直接将布尔型series变为了0/1数字，非常方便的实现了二分类特征的编码。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-01T07:48:20.891932Z",
     "start_time": "2024-03-01T07:48:20.879561Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将三分类变量转换为数值型变量\n",
    "\n",
    "data[\"Embarked\"].isnull().sum() #查看缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-28T13:44:10.255393Z",
     "start_time": "2024-02-28T13:44:10.236265Z"
    }
   },
   "outputs": [],
   "source": [
    "data = data[data[\"Embarked\"].notnull()]  #删除缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-28T13:44:10.622495Z",
     "start_time": "2024-02-28T13:44:10.611232Z"
    }
   },
   "outputs": [],
   "source": [
    "labels = data[\"Embarked\"].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-01T08:06:16.256653Z",
     "start_time": "2024-03-01T08:06:16.244771Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# labels = data[\"Embarked\"].unique().tolist()\n",
    "labels.index(\"Q\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-01T08:04:38.272952Z",
     "start_time": "2024-03-01T08:04:38.263900Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['S', 'C', 'Q']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# labels labels[0]-> S  labels[1] -> C labels[2] -> Q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-01T08:07:05.783421Z",
     "start_time": "2024-03-01T08:07:05.770943Z"
    }
   },
   "outputs": [],
   "source": [
    "data[\"Embarked\"] = data[\"Embarked\"].apply(lambda x:labels.index(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-01T08:07:13.236046Z",
     "start_time": "2024-03-01T08:07:13.217160Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived  Pclass  Sex   Age  SibSp  Parch     Fare  Embarked\n",
       "PassengerId                                                              \n",
       "1                   0       3    1  22.0      1      0   7.2500         0\n",
       "2                   1       1    0  38.0      1      0  71.2833         1\n",
       "3                   1       3    0  26.0      0      0   7.9250         0\n",
       "4                   1       1    0  35.0      1      0  53.1000         0\n",
       "5                   0       3    1  35.0      0      0   8.0500         0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-28T13:44:10.824197Z",
     "start_time": "2024-02-28T13:44:10.817613Z"
    }
   },
   "outputs": [],
   "source": [
    "# data[\"Embarked\"] = data[\"Embarked\"].apply(lambda x: labels.index(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-28T13:44:11.006193Z",
     "start_time": "2024-02-28T13:44:10.984975Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived  Pclass  Sex   Age  SibSp  Parch     Fare  Embarked\n",
       "PassengerId                                                              \n",
       "1                   0       3    1  22.0      1      0   7.2500         0\n",
       "2                   1       1    0  38.0      1      0  71.2833         1\n",
       "3                   1       3    0  26.0      0      0   7.9250         0\n",
       "4                   1       1    0  35.0      1      0  53.1000         0\n",
       "5                   0       3    1  35.0      0      0   8.0500         0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看处理后的数据集\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 提取标签和特征矩阵，切分测试集和训练集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-01T08:19:24.266640Z",
     "start_time": "2024-03-01T08:19:24.254771Z"
    }
   },
   "outputs": [],
   "source": [
    "# X = data.iloc[:,data.columns != \"Survived\"]\n",
    "# y = data.iloc[:,data.columns == \"Survived\"]\n",
    "# data.columns != \"Survived\"\n",
    "X = data.iloc[:,data.columns != \"Survived\"]\n",
    "y = data.iloc[:,data.columns == \"Survived\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-01T08:21:02.551156Z",
     "start_time": "2024-03-01T08:21:02.526404Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>699</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>110.8833</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>771</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.2167</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>629</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>801</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7958</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>622 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Pclass  Sex   Age  SibSp  Parch      Fare  Embarked\n",
       "PassengerId                                                     \n",
       "699               1    1  49.0      1      1  110.8833         1\n",
       "67                2    0  29.0      0      0   10.5000         0\n",
       "771               3    1  24.0      0      0    9.5000         0\n",
       "159               3    1   NaN      0      0    8.6625         0\n",
       "139               3    1  16.0      0      0    9.2167         0\n",
       "...             ...  ...   ...    ...    ...       ...       ...\n",
       "629               3    1  26.0      0      0    7.8958         0\n",
       "801               2    1  34.0      0      0   13.0000         0\n",
       "577               2    0  34.0      0      0   13.0000         0\n",
       "392               3    1  21.0      0      0    7.7958         0\n",
       "74                3    1  26.0      1      0   14.4542         1\n",
       "\n",
       "[622 rows x 7 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xtrain, Xtest, Ytrain, Ytest = train_test_split(X,y,test_size=0.3,random_state=420)\n",
    "# Xtrain.shape\n",
    "# Xtest.shape\n",
    "# Xtrain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-01T08:22:04.431979Z",
     "start_time": "2024-03-01T08:22:04.423311Z"
    }
   },
   "outputs": [],
   "source": [
    "#修正测试集和训练集的索引\n",
    "for i in [Xtrain, Xtest, Ytrain, Ytest]:\n",
    "    i.index = range(i.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-01T08:22:53.792112Z",
     "start_time": "2024-03-01T08:22:53.772387Z"
    },
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
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       "  </tbody>\n",
       "</table>\n",
       "<p>622 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived\n",
       "0           0\n",
       "1           1\n",
       "2           0\n",
       "3           0\n",
       "4           0\n",
       "..        ...\n",
       "617         0\n",
       "618         0\n",
       "619         1\n",
       "620         1\n",
       "621         0\n",
       "\n",
       "[622 rows x 1 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ytrain\n",
    "# age 列数据的填充\n",
    "Xtrain[\"age\"].fillna(Xtrain[\"age\"].mean())\n",
    "Xtest[\"age\"].fillna(Xtrain[\"age\"].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-01T08:31:42.481893Z",
     "start_time": "2024-03-01T08:31:42.466386Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass        0\n",
       "Sex           0\n",
       "Age         120\n",
       "SibSp         0\n",
       "Parch         0\n",
       "Fare          0\n",
       "Embarked      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 缺失值检查\n",
    "Xtrain.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-01T08:31:45.757458Z",
     "start_time": "2024-03-01T08:31:45.739280Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass       0\n",
       "Sex          0\n",
       "Age         57\n",
       "SibSp        0\n",
       "Parch        0\n",
       "Fare         0\n",
       "Embarked     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 缺失值检查\n",
    "Xtest.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-01T08:34:33.383813Z",
     "start_time": "2024-03-01T08:34:33.363254Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#处理缺失值，对含有缺失值的列进行填补\n",
    "Xtrain.loc[:,\"Age\"] = Xtrain[\"Age\"].fillna(Xtrain[\"Age\"].mean())\n",
    "Xtest.loc[:,\"Age\"] = Xtest[\"Age\"].fillna(Xtrain[\"Age\"].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-01T08:34:41.044454Z",
     "start_time": "2024-03-01T08:34:41.026952Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass      0\n",
       "Sex         0\n",
       "Age         0\n",
       "SibSp       0\n",
       "Parch       0\n",
       "Fare        0\n",
       "Embarked    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xtest.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 3.5 建模并进行模型优化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-01T08:35:27.797521Z",
     "start_time": "2024-03-01T08:35:27.773778Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7453183520599251"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = DTC(random_state=25)\n",
    "clf = clf.fit(Xtrain, Ytrain)\n",
    "score_ = clf.score(Xtest, Ytest)\n",
    "score_\n",
    "\n",
    "# baseline 0.7453183520599251\n",
    "# criterion splitter max_depth min_samples_split min_samples_leaf\n",
    "# 2          2       (5-10) 5   (10-20) 10        (10-20) 10\n",
    "# 2*2*5*10*10\n",
    "# 使用计算机完成对应参数的枚举，我们称这种方法叫做网格搜索：对模型的参数进行枚举"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-01T08:52:36.981093Z",
     "start_time": "2024-03-01T08:49:43.037211Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=DecisionTreeClassifier(random_state=25),\n",
       "             param_grid={'criterion': ('gini', 'entropy'),\n",
       "                         'max_depth': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
       "                         'min_impurity_decrease': [0.0, 0.02631578947368421,\n",
       "                                                   0.05263157894736842,\n",
       "                                                   0.07894736842105263,\n",
       "                                                   0.10526315789473684,\n",
       "                                                   0.13157894736842105,\n",
       "                                                   0.15789473684210525,\n",
       "                                                   0.18421052631578946,\n",
       "                                                   0.21052631578947367,\n",
       "                                                   0.23684210526315788,\n",
       "                                                   0.2631578947368421,\n",
       "                                                   0.2894736842105263,\n",
       "                                                   0.3157894736842105,\n",
       "                                                   0.3421052631578947,\n",
       "                                                   0.3684210526315789,\n",
       "                                                   0.39473684210526316,\n",
       "                                                   0.42105263157894735,\n",
       "                                                   0.4473684210526315,\n",
       "                                                   0.47368421052631576, 0.5],\n",
       "                         'min_samples_leaf': [1, 6, 11, 16, 21, 26, 31, 36, 41,\n",
       "                                              46],\n",
       "                         'splitter': ('best', 'random')})"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# 参数空间\n",
    "# gini_thresholds = np.linspace(0,0.5,20)\n",
    "parameters = {'splitter':('best','random')\n",
    ",'criterion':(\"gini\",\"entropy\")\n",
    " ,\"max_depth\":[*range(1,11)]\n",
    " ,'min_samples_leaf':[*range(1,50,5)]\n",
    " ,'min_impurity_decrease':[*np.linspace(0,0.5,20)]} # 8000\n",
    "\n",
    "clf = DTC(random_state=25)\n",
    "# clf.fit(x,y)\n",
    "# cv=10 表示将数据分成10份，取出其中的9份作为训练，其中的一份作为测试，这个过程会执行10次，让模型100%的计算一遍数据\n",
    "GS = GridSearchCV(clf, parameters, cv=10) # 10\n",
    "GS.fit(Xtrain,Ytrain)\n",
    " \n",
    "\n",
    "# [*range(1,11)]\n",
    "# [*range(1,50,5)]\n",
    "# [*np.linspace(0,0.5,20)]\n",
    "\n",
    "# clf = DTC(random_state=25)\n",
    "# GS = GridSearchCV(clf, parameters, cv=10)\n",
    "# GS.fit(Xtrain,Ytrain)\n",
    "# # GS.best_params_\n",
    "# GS.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-03-01T08:53:11.392337Z",
     "start_time": "2024-03-01T08:53:11.381258Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'entropy',\n",
       " 'max_depth': 8,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_samples_leaf': 6,\n",
       " 'splitter': 'best'}"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# GS.best_score_\n",
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-28T13:44:14.214857Z",
     "start_time": "2024-02-28T13:44:14.069881Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8052434456928839\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 在不同 max_depth下观察模型的拟合状况\n",
    "tr = []\n",
    "te = []\n",
    "for i in range(1,11):\n",
    "    clf = DTC(random_state=0,max_depth=i)\n",
    "    clf = clf.fit(Xtrain, Ytrain)\n",
    "    score_tr = clf.score(Xtrain,Ytrain)\n",
    "    score_te = clf.score(Xtest,Ytest)\n",
    "    tr.append(score_tr)\n",
    "    te.append(score_te)\n",
    "print(max(te))\n",
    "plt.plot(range(1,11),tr,color=\"red\",label=\"train\")\n",
    "plt.plot(range(1,11),te,color=\"blue\",label=\"test\")\n",
    "plt.xticks(range(1,11))\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-28T13:44:14.999860Z",
     "start_time": "2024-02-28T13:44:14.723488Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8014981273408239\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 在不同 min_samples_leaf下观察模型的拟合状况\n",
    "tr = []\n",
    "te = []\n",
    "for i in range(1,30):\n",
    "    clf = DTC(random_state=0\n",
    "#               ,max_depth=3\n",
    "              ,min_samples_leaf=i)\n",
    "    clf = clf.fit(Xtrain, Ytrain)\n",
    "    score_tr = clf.score(Xtrain,Ytrain)\n",
    "    score_te = clf.score(Xtest,Ytest)\n",
    "    tr.append(score_tr)\n",
    "    te.append(score_te)\n",
    "print(max(te))\n",
    "plt.plot(range(1,30),tr,color=\"red\",label=\"train\")\n",
    "plt.plot(range(1,30),te,color=\"blue\",label=\"test\")\n",
    "plt.xticks(range(1,30)[::2])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-28T13:44:15.668129Z",
     "start_time": "2024-02-28T13:44:15.482733Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8089887640449438\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 在不同 min_samples_split下观察模型的拟合状况\n",
    "tr = []\n",
    "te = []\n",
    "for i in range(2,30):\n",
    "    clf = DTC(random_state=0\n",
    "#               ,max_depth=3\n",
    "              ,min_samples_split=i)\n",
    "    clf = clf.fit(Xtrain, Ytrain)\n",
    "    score_tr = clf.score(Xtrain,Ytrain)\n",
    "    score_te = clf.score(Xtest,Ytest)\n",
    "    tr.append(score_tr)\n",
    "    te.append(score_te)\n",
    "print(max(te))\n",
    "plt.plot(range(2,30),tr,color=\"red\",label=\"train\")\n",
    "plt.plot(range(2,30),te,color=\"blue\",label=\"test\")\n",
    "plt.xticks(range(2,30)[::2])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-28T13:44:15.963407Z",
     "start_time": "2024-02-28T13:44:15.858321Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7752808988764045\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 在不同 max_features下观察模型的拟合状况\n",
    "tr = []\n",
    "te = []\n",
    "for i in range(1,8):\n",
    "    clf = DTC(random_state=0\n",
    "#               ,max_depth=3\n",
    "              ,max_features=i)\n",
    "    clf = clf.fit(Xtrain, Ytrain)\n",
    "    score_tr = clf.score(Xtrain,Ytrain)\n",
    "    score_te = clf.score(Xtest,Ytest)\n",
    "    tr.append(score_tr)\n",
    "    te.append(score_te)\n",
    "print(max(te))\n",
    "plt.plot(range(1,8),tr,color=\"red\",label=\"train\")\n",
    "plt.plot(range(1,8),te,color=\"blue\",label=\"test\")\n",
    "plt.xticks(range(1,8))\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-28T13:44:16.502593Z",
     "start_time": "2024-02-28T13:44:16.340747Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7790262172284644\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 在不同 min_impurity_decrease下观察模型的拟合状况\n",
    "tr = []\n",
    "te = []\n",
    "for i in np.linspace(0,0.2,20):\n",
    "    clf = DTC(random_state=0\n",
    "#               ,max_depth=3\n",
    "              ,min_impurity_decrease=i)\n",
    "    clf = clf.fit(Xtrain, Ytrain)\n",
    "    score_tr = clf.score(Xtrain,Ytrain)\n",
    "    score_te = clf.score(Xtest,Ytest)\n",
    "    tr.append(score_tr)\n",
    "    te.append(score_te)\n",
    "print(max(te))\n",
    "plt.plot(np.linspace(0,0.2,20),tr,color=\"red\",label=\"train\")\n",
    "plt.plot(np.linspace(0,0.2,20),te,color=\"blue\",label=\"test\")\n",
    "plt.xticks(np.round(np.linspace(0,0.2,20),2)[::2])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 不同剪枝参数对决策树模型的影响程度"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| <font color=\"orange\" size=3>**参数**<font> |<font color=\"orange\" size=3>调参方向<font>| <font color=\"orange\" size=3>对模型的影响程度<font>|\n",
    "| --------------------------------- |---------|----|\n",
    "|max_depth|默认最大深度，<br>通常调参会往减小max_depth的方向进行|⭐⭐⭐⭐|\n",
    "|max_features|默认为None，指全部特征<br>通常调参会往减小max_features的方向进行|⭐⭐⭐|\n",
    "|min_impurity_decrease|默认为0.0，<br>通常调参会往增大min_impurity_decrease的方向进行|⭐⭐⭐|\n",
    "|min_samples_split|默认为2，<br>通常调参会往增大min_samples_split的方向进行|⭐⭐|\n",
    "|min_samples_leaf|默认为1，<br>通常调参会往增大min_samples_leaf的方向进行|⭐⭐|"
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